This PDF file contains the front matter associated with SPIE Proceedings Volume 8672, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

A dynamic cardiac phantom can play a significant role in the evaluation and development of ultrasound and cardiac magnetic resonance (MR) motion tracking and registration methods. A four chamber multimodal cardiac phantom has been designed and built to simulate normal and pathologic hearts with different degrees of “infarction” and “scar tissues”. In this set up, cardiac valves have been designed and modeled as well. The four-chamber structure can simulate the asymmetric ventricular, atrial and valve motions. Poly Vinyl Alcohol (PVA) is used as the principal material since it can simulate the shape, elasticity, and MR and ultrasound properties of the heart. The cardiac shape is simulated using a four-chamber mold made of polymer clay. An additional pathologic heart phantom containing stiff inclusions has been manufactured in order to simulate an infracted heart. The stiff inclusions are of different shapes and different degrees of elasticity and are able to simulate abnormal cardiac segments. The cardiac elasticity is adjusted based on freeze-thaw cycles of the PVA cryogel for normal and scarred regions. Ultrasound and MRI markers were inserted in the cardiac phantom as landmarks for validations. To the best of our knowledge, this is the first multimodal phantom that models a dynamic four-chamber human heart including the cardiac valve.

Purpose: We describe the initial assessment of the peripheral quantitative CT (pQCT) imaging capabilities of a conebeam CT (CBCT) scanner dedicated to musculoskeletal extremity imaging. The aim is to accurately measure and quantify bone and joint morphology using information automatically acquired with each CBCT scan, thereby reducing the need for a separate pQCT exam. Methods: A prototype CBCT scanner providing isotropic, sub-millimeter spatial resolution and soft-tissue contrast resolution comparable or superior to standard multi-detector CT (MDCT) has been developed for extremity imaging, including the capability for weight-bearing exams and multi-mode (radiography, fluoroscopy, and volumetric) imaging. Assessment of pQCT performance included measurement of bone mineral density (BMD), morphometric parameters of subchondral bone architecture, and joint space analysis. Measurements employed phantoms, cadavers, and patients from an ongoing pilot study imaged with the CBCT prototype (at various acquisition, calibration, and reconstruction techniques) in comparison to MDCT (using pQCT protocols for analysis of BMD) and micro-CT (for analysis of subchondral morphometry). Results: The CBCT extremity scanner yielded BMD measurement within ±2-3% error in both phantom studies and cadaver extremity specimens. Subchondral bone architecture (bone volume fraction, trabecular thickness, degree of anisotropy, and structure model index) exhibited good correlation with gold standard micro-CT (error ~5%), surpassing the conventional limitations of spatial resolution in clinical MDCT scanners. Joint space analysis demonstrated the potential for sensitive 3D joint space mapping beyond that of qualitative radiographic scores in application to non-weight-bearing versus weight-bearing lower extremities and assessment of phalangeal joint space integrity in the upper extremities. Conclusion: The CBCT extremity scanner demonstrated promising initial results in accurate pQCT analysis from images acquired with each CBCT scan. Future studies will include improved x-ray scatter correction and image reconstruction techniques to further improve accuracy and to correlate pQCT metrics with known pathology.

Purpose: To determine optimal collimator and gamma camera combination for Ioflupane I-123 (DaTscan) striatal SPECT. Methods: Anthropomorphic basal ganglia phantom was used. The striatal chambers (caudate and putamen chambers) and the large chamber simulating nonspecific background activity in the remainder of the brain were filled with I-123 with the specific activity ratio 7.7. SPECT data were acquired using triple-head gamma camera (THGC) with fan-beam low-energy ultra high-resolution (LEUR) collimators, with dual-head gamma camera (DHGC) with parallel-beam lowenergy high-resolution (LEHR) collimators and medium-energy general-purpose (MEGP) collimators. Data were acquired at 159 keV with a 20% window, with I-123 and Tc-99m flood table for THGC and DHGC, respectively. The images were reconstructed using the OSEM algorithm with resolution modeling and uniform attenuation correction, and Butterworth postfilter, 5th order and 0.64, 0.78 and 1.0 Ny for LEUR, LEHR, and MEGP, respectively. The filter parameters were chosen to optimize the balance between image noise and spatial resolution. Results: The best image quality in terms of spatial resolution and contrast was obtained with fan-beam LEUR/THGC. The MEGP/DHBC produced images with better contrast-to-noise ratio than LEHR/DHGC. The measured ratio of mean activity in striatal chambers to the remainder of the brain was comparable for all three collimator/camera combinations. Conclusions: Based on phantom DaTscan striatal SPECT, the THGC with fan-beam LEUR collimators is preferable. If DHGC is used MEGP collimators provide better image quality, as compared to LEHR. More studies including patient studies are needed to confirm best collimator/camera combination.

Renal artery stenosis (RAS) promotes fibrosis by excessive and irreversible collagen deposition which may lead to end stage renal failure. Currently, invasive tissue biopsy is the main tool to assess fibrosis. Magnetization transfer imaging (MTI) is a MR-based technique that is sensitive to the interaction of macromolecules (e.g. collagen) and free water. The characteristics of these interactions are notoriously organ and tissue dependent. This study tested the hypothesis that using MTI, renal fibrosis in RAS can be detected. MTI was applied in mice (n=7) with unilateral RAS induced by partial occlusion of the renal artery. In off-resonance MTI, to achieve highest sensitivity, offset frequency, RF pulse power and bandwidth were optimized towards enhancing the contrast between the fibrotic and non-fibrotic tissue. Moreover, magnetization exchange rates (k_f and k_r) and the fractional size of the restricted magnetization (F), as markers of tissue molecular-morphological change, were estimated using steady-state free precession, on-resonance MTI. The optimal contrast for visual differentiation was achieved at offset frequency, RF pulse power, and effective bandwidth of 6.6kHz, 10μT, and 300Hz, respectively. On-resonance MT demonstrated significantly higher F and k_f in the stenotic vs. the contra-lateral kidney. Therefore, off-resonance MT can qualitatively differentiate the fibrotic from the non-fibrotic tissue. Furthermore, k_f and F may serve as biomarkers for kidney morphological changes caused by RAS.

Microfluidic mixing or mixing at low Reynolds number is dominated by viscous forces that prevent turbulent flow. It therefore differs from conventional mixing (e.g., stirring milk into coffee), as it is driven primarily by diffusion. Diffusion is in turn dependent on (i) the concentration gradient along the interface between two fluids (dye front line) and (ii) the extent of the interface itself. Previously, we proposed an in vivo method to microscopically monitor the mixing interface using Shannon information entropy as mixing indicator and explored the use of length of dye front line as an indirect measure of mixing efficiency. In this work, we present a robust image processing chain supporting quantitative measurements. Based on data from ciliated surfaces mixing dye and water, the dye-water interface front line is extracted automatically using the following processing steps: (i) noise reduction (average filtering) and down sampling in time to reduce compression artifacts; (ii) subtraction imaging with key reference frames in RGB color space to remove background; (iii) segmentation of dye based on color saturation in HSV color space; (iv) extraction of front line; (v) curve smoothing in curvature scale space (CSS) with an improved Gaussian filter adaptive to the local concentration gradient; and (vi) extraction of length. Evaluation is based on repeated measurements. Reproducibility in unaltered animals is shown using intra- and inter-animal comparison. Future work will include a more comprehensive evaluation and the application to datasets with multiple classes.

To improve the definition of the geometrical and mechanical properties of the porcine pulmonary arteries, we utilized an in vivo imaging-based approach to quantify the influence of static extravascular pressure change on pulmonary arterial geometry. The cross-sectional area and distance from the inlet of pulmonary arteries of two animals were measured over a range of static airway inflation pressure (7 cmH₂O - 25 cmH₂O, i.e. 0.69 kPa – 2.45 kPa). Vessels with diameter range of approximately 2.0 mm to 5.5 mm at airway inflation pressure of 25 cmH₂O (2.45kPa) were considered. The results suggest that lung inflation stretches the vessels laterally, but has no statistically significant effect on diameter.

In management of cardiovascular diseases, information about the patient-specific behavior of blood flow and pressure can be essential. In the human aorta, velocity-encoded magnetic resonance imaging (MRI) is the only method capable of measuring complete time-resolved three-dimensional vector fields of the blood flow velocities. Additionally, computations of relative blood pressure from this data source have been presented in recent years. Thus, velocityencoded MRI can be a valuable measurement technique for both blood flow and blood pressure values in diagnostics and therapy control of aortic diseases. In the last years, we have developed the software framework MEDIFRAME for cardiovascular diagnostics based on MRI acquisitions. In this article, we apply our in-house developed software framework for a MRI-based hemodynamic analysis in five patients with surgically treated aortic coarctations. We compared our results to a control group of five healthy volunteers. The study included the measurement of blood flow velocities by phase-contrast MRI and the subsequent computation of relative blood pressure values. We generated a set of suitable visualizations for flow and pressure and created centerline diagrams of the cross-sectional area, flow and mean relative blood pressure. Additionally, characteristic values were computed from the centerline diagrams for every subject. In the vast majority of the visualization and quantification techniques of our software framework, we observed significant effects of the treated aortic coarctations. Therefore, we draw the conclusion that this kind of MRI-based hemodynamic analysis can be a valuable tool for diagnostics and therapy control of aortic coarctations.

Use of Phase contrast (PC) MRI in measurement of blood flow has significant clinical importance. In this paper, we compare the accuracy of the conventional approach to flow imaging to two de novo approaches in 3 normal subjects in the common, internal, and external carotid arteries and discuss and demonstrate the advantages and disadvantages of each method. The conventional PC sequence adopts a Cartesian read-out in k-space and requires longer acquisitions but exhibits flow artifacts in the setting of stenotic and disturbed flow. Spiral PC collects k-space data using spiral readout and is capable of reducing the TR and TE in order to minimize the total imaging time. Despite its efficiency in scan time, in the single shot mode, this technique suffers from off-resonance and inconsistent data artifacts. Use of multiple short spiral arms for providing k-space coverage resolves these issues. Ultra short TE (UTE) PC MRI is a novel technique which adopts a radial trajectory and provides improvements to the standard radial acquisition by reducing the echo time to less than 1 ms through combination of flow encoding and slice select gradients and by immediate sampling of the FID during readout. The ultra-short echo times, improves on intravoxel spin dephasing due to fluid mixing observed in imaging of disturbed flow and stenotic jets. Despite its capability of achieving the shortest TE, this method is hindered by longer acquisition times and phase corruption errors. We mitigate this by a novel 3-D acquisition which includes a phase correction step. All three approaches were found to be able to quantify the normal Carotid flow waveform with high accuracy.

Magnetic resonance tagging techniques have been widely used for measuring cardiac motion. We propose a novel 3D sine wave modeling (3D SinMod) approach to automatic analysis of cardiac deformations. An accelerated 3D complementary spatial modulation of magnetization (CSPAMM) tagging technique1 was used to modulate the myocardial tissue and to acquire 3D MR data sets of the whole-heart including three orthogonal tags within three breath-holds. Each tag set is able to assess the motion along a direction perpendicular to the tag lines. With the application of CSPAMM, the effect of tag fading due to T₁ relaxation is mitigated and tag deformations can be visualized for the entire cardiac cycle, including diastolic phases. In the proposed approach, the environment around each voxel in the 3D volume is modeled as a moving sine wavefront with local frequency and amplitude. The entire framework, from data acquisition to data analysis is in 3D domain, which permits quantification of both the in-plane and through-plane motion components. The accuracy and the effectiveness of the proposed method has been validated using both simulated and in vivo data.

Quantitative motion analysis of echocardiographic images helps clinicians with the diagnosis and therapy of patients suffering from cardiac disease. Quantitative analysis is usually based on TDI (Tissue Doppler Imaging) or speckle tracking. These methods are based on two independent techniques – the Doppler Effect and image registration, respectively. In order to increase the accuracy of the speckle tracking technique and cope with the angle dependency of TDI, herein, a combined approach dubbed TDIOF (Tissue Doppler Imaging Optical Flow) is proposed. TDIOF is formulated based on the combination of B-mode and Doppler energy terms in an optical flow framework and minimized using algebraic equations. In this paper, we report on validations with simulated, physical cardiac phantom, and in-vivo patient data. It is shown that the additional Doppler term is able to increase the accuracy of speckle tracking, the basis for several commercially available echocardiography analysis techniques.

This study was motivated by anecdotal reports from our clinicians that the lung parenchyma appears “different” (more heterogeneous) in asthmatics compared to non-asthmatics. We investigated whether traditional texture features were different between severe asthmatics and non-asthmatics. CT examinations from 76 subjects classified as “severe asthma” (n = 51) and “normal control” (n = 25) based on Severe Asthma Research Program (SARP) criteria were used in this study. The CT exams were performed on a 64-detector or 16-detector GE scanner at a radiation exposure of 96.6 (±30.7) mAs with the subjects holding their breath at end-normal-expiration (functional residual capacity). The CT images were reconstructed at 0.625 or 1.25 mm thickness using either GE’s “standard” or “detail” kernels. Air trapping was computed as the percentage of voxels with a value less than -856 HU. Gray level co-occurrence matrices (GLCM) were computed from the CT images, and 15 Haralick texture descriptors were computed from the GLCM. Air trapping was significantly greater in the severe asthma subjects compared to the normal control subjects. Seven of the 15 texture features were significantly different between the severe asthma and normal control subjects. Our findings provide some validity to anecdotal reports of differences between the parenchyma of asthmatic and non-asthmatics. The significant texture features may ultimately be used to classify individuals as asthmatic or non-asthmatic, which should improve the limited performance of air trapping alone.

Alzheimer’s disease (AD) is caused by pathological changes including cortical thinning occurring throughout the brain. Traditional methods for assessing cortical thickness are challenged by the sub-millimeter accuracy required for clinical conditions and the convoluted nature of brain surface. Furthermore, there is a significant overlap of gray and white matter intensities. A novel Brain Surface Intensity Model (BSIM) has been developed for use as a potential imaging biomarker for neurodegenerative diseases. BSIM technique extracts MR intensity profiles perpendicular to a mathematically defined gray matter iso-intensity layer (GMIIL) at predefined reference points, fits that profile to BSIM, and computes cortical thickness. A 3D visualization tool has been developed to evaluate intensity extraction and model calculation. 29 normal subjects aged between 70 to 80 years from ADNI database were used to generate normal references and measure individual Z-score cortical thinning. 30 age-matched AD subjects were used to study thinning patterns. Significant cortical thinning (p < 0.0001) was found for AD group. 95% confidence interval of the cortical thinning in AD patients was from 0.17 to 0.23 mm. The cortical thinning of the AD patients showed distinct features that differentiate AD patients from normal controls. The thickness measurements of 29 normal controls were validated by comparing with results from literature (p = 0.94). BSIM technique avoids complicated 3D segmentation of brain gray and white matters, and simplifies the thickness calculation. Moreover, it is less affected by the image noise, inhomogeneity, partial volume effects, and the intensity overlap of the white and gray matters.

Golden retriever muscular dystrophy (GRMD) is a canine model of Duchenne muscular dystrophy (DMD) that has been increasingly used in both pathogenetic and therapeutic pre-clinical studies. Recent studies have shown that Magnetic resonance imaging (MRI) has great potential to noninvasively assess muscle disorders and has been increasingly used to monitor disease progression in DMD patients and GRMD dogs. In this study, we developed a statistical texture analysis based MRI quantification framework for GRMD. Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a natural history study. The dogs were longitudinally scanned at 3, 6 and 9 months of age. We first segmented six proximal limb muscles of each dog using a semi-automated, interpolation-based method and then automatically measured the 3D first-order histogram and novel 3D high-order run-length matrix based texture features within each segmented muscle. Our results indicated that MRI texture features has the ability to distinguish the normal and GRMD muscles at each age. Our experimental results demonstrated the potential of MRI texture measurements to serve as biomarkers to distinguish normal and muscular dystrophic muscles in DMD patients.

The interest of our study is the in-vivo transcranial visualization of blood flow without removal of the skull. The strong attenuation, scattering, and distortion by the skull bones (or other tissues) make it difficult to use currently existing methods. However, blood flow can still be detected by using the ultrasonic speckle reflections from the blood cells and platelets (or contrast agents) moving with the blood. The methodology specifically targets these random temporal changes, imaging the owing region and eliminating static components. This process analyzed over multiple exposures allows an image of the blood flow to be obtained, even with negative acoustic effects of the skull in play. Experimental results show this methodology is able to produce both 2D and 3D images of the owing region, and eliminates those regions of static acoustic sources as predicted. Images produced of the owing region are found to agree with the physical size of the vessel analogues, and also found to provide a qualitative measure on the amount of flow through the vessels.

Approximately 1 in 8 women will develop breast cancer in their lifetime. Estimates suggest 230,500 new cases of invasive breast cancer in 2011, resulting in approximately 40,000 deaths. Traditional screening technologies, such as X-ray mammography use ionizing radiation and suffer from high false-positive and false-negative rates. Due to the high contrast that exists between the dielectric properties of normal and abnormal breast tissue, microwave-imaging spectroscopy has proven an attractive breast cancer imaging modality. We have shown that the incorporation of a volume’s internal structural information into our image reconstruction algorithm can increase the accuracy of recovered dielectric properties. Additionally, image reconstruction has benefited from the use of a custom reconstruction mesh generated from the imaged volume’s perimeter boundary. This information is used in a conformal microwave image (CMI) reconstruction process, and has increased the accuracy of recovered high contrast regions within the volume’s perimeter without the use of prior internal spatial information. In simulation and phantom experiments with regular geometries, boundary information is obtained through spatial measurements. For irregularly shaped boundaries, alternative means are necessary for accurate boundary extraction. In this paper we demonstrate the MR-guided CMI reconstruction process for an irregularly shaped boundary; boundary information extracted from MR images will be used to generate a custom boundary-derived mesh for microwave image reconstruction. Results from images reconstructed using the MR-guided CMI reconstruction process will be compared with uniformly reconstructed images, highlighting the increased accuracy of high contrast features within the volume without the use of prior internal spatial information.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

Numerous studies have explored the relationship between cortical structure and brain development, cognitive function, and functional connectivity. The highly convoluted cortical topography makes manual measurements arduous and often impractical given the population sizes necessary for sufficient statistical power. Computational techniques have permitted large-scale studies as they provide robust and reliable localized measurements characterizing the cortex with little or no human intervention. Particularly useful to the neuroscience community are publicly available tools, such as the popular surface-based Freesurfer, which facilitate the testing and refinement of hypotheses. In this paper, we introduce the volume-based Advanced Normalization Tools (ANTs) cortical thickness automated pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness) all developed as part of the ANTs open science effort. Complementing the open source aspect of ANTs we demonstrate its utility using the publicly available IXI data set.

Radiation induced lung injury (RILI) is a common finding following lung radiotherapy and results in radiographic changes on computed tomography (CT). Stereotactic ablative radiotherapy (SABR) treats the tumour to a highly conformal dose with large doses/fraction, which can result in benign, tumour-mimicking radiographic changes. Our purpose was to determine the ability of quantitative measures of post-SABR radiographic changes to distinguish the subject groups (recurrence vs. RILI) at several time points. Two regions were manually contoured on each follow-up CT: consolidative changes and ground glass opacity (GGO). A peri-tumoural region of GGO was also taken around the consolidative changes. At 9 months, patients with recurrence had significantly denser consolidative areas compared to patients with RILI (p=.046) and significantly increased variability of CT densities in the GGO areas (p=.0078). The variability of CT density in a peri-tumoural region of 4 mm thickness was also significant at 9 months post-treatment (p=.0499). Our preliminary study of classification accuracy based on these measures showed that variability of the GGO CT density was the best predictor with a cross validation error of 26.1%, demonstrating that further refinement of the features and classifier may soon lead to a clinically useful computer-aided diagnosis tool. These results suggest the future potential to distinguish patients with recurrence from those RILI at 9 months post-SABR based on appearance characteristics within the consolidative, GGO, and peri-tumoural regions. This could potentially allow for earlier salvage of patients with recurrence, and result in fewer investigations of benign RILI.

Statistical shape analysis has emerged as an insightful method for evaluating brain structures in neuroimaging studies, however most shape frameworks are surface based and thus directly depend on the quality of surface alignment. In contrast, medial descriptions employ thickness information as alignment-independent shape metric. We propose a joint framework that computes local medial thickness information via a mean latitude axis from the well-known spherical harmonic (SPHARM-PDM) shape framework. In this work, we applied SPHARM derived medial representations to the morphological analysis of lateral ventricles in neonates. Mild ventriculomegaly (MVM) subjects are compared to healthy controls to highlight the potential of the methodology. Lateral ventricles were obtained from MRI scans of neonates (9-144 days of age) from 30 MVM subjects as well as age- and sex-matched normal controls (60 total). SPHARM-PDM shape analysis was extended to compute a mean latitude axis directly from the spherical parameterization. Local thickness and area was straightforwardly determined. MVM and healthy controls were compared using local MANOVA and compared with the traditional SPHARM-PDM analysis. Both surface and mean latitude axis findings differentiate successfully MVM and healthy lateral ventricle morphology. Lateral ventricles in MVM neonates show enlarged shapes in tail and head. Mean latitude axis is able to find significant differences all along the lateral ventricle shape, demonstrating that local thickness analysis provides significant insight over traditional SPHARM-PDM. This study is the first to precisely quantify 3D lateral ventricle morphology in MVM neonates using shape analysis.

Spike EEG source localization results are influenced by different errors and approximations, e.g., head-model complexity, EEG signal noise, electrode misplacements, tissue anisotropy, tissue conductivity noise as well as numerical errors. For accurate source localization, understanding the affects of these errors on the source localization is very crucial. Six finite element head models are selected for a head-model complexity study. A reference head model is used to create the synthetic EEG signals by placing a dipole inside the model to mimic the epileptic spike activity. To understand the influence of EEG signal noise, tissue conductivity noise and electrode misplacements on the EEG source localization, different level of noises are added to EEG signals, tissue conductivities and electrode positions, independently. To investigate the influence of white matter anisotropy, a realistic head model generated from T1-weighted MRI is used and the conductivity anisotropy for the white matter is calculated from diffusion tensor imaging (DTI). Major findings of the study include (1) the CSF layer plays an important role to achieve an accurate source localization result, (2) the source localization is very sensitive to the tissue conductivity noises, (3) one centimeter electrode misplacement cause approximately 8 mm localization error, (4) the source localization is robust with respect to the EEG signal noise and (5) the model with white matter anisotropy has small source localization error but large amplitude and orientation errors compared to the isotropic head model.

The functional magnetic resonance imaging (fMRI) research on face processing have found that the significant activation by face stimuli mainly locailized at the occipital temporal lobe especilly the fusiform gyrus. However, fMRI cannot reflect the face processing as time changes. Event-related potential (ERP) can record electrophysiological changes induced by neuronal activation in time, but spatial information is not well localized. Fusing fMRI and ERP data can perform that how the fMRI activation changes as time move at each ERP time point. Although most of fuse methods perform to analysis by constraint ERP or fMRI data, joint independent component analysis (jICA) method can equally use the ERP and fMRI data and simultaneously examine electrophysiologic and hemodynamic response. In this paper, we use jICA method to analysis two modalities in common data space in order to examine the dynamics of face stimuli response. The results showed that the ERP component N170 response associated with middle occipital gyrus, fusiform gyrus, inferior occipital gyrus, superior temporal gyrus and parahippocampa gyrus for face. Likewise, for non-face, the N170 component was mainly related to parahippocampa gyrus, middle occipital gyrus and inferior occipital gyrus. Further studying on the correlation of the localized ERP response and corresponding average ERP, it was also concluded that the spatial activations related to N170 response induced by face stimulus located in fusiform gyrus, and that induced by non-face stimulus located in parahippocampa gyrus. From the result, fusing fMRI and ERP data by jICA not only provides the time information on fMRI and the spatial source of ERP component, but also reflects spatiotemporal change during face processing.

Real-time functional magnetic resonance imaging (fMRI) is a useful tool that researchers can monitor and assess dynamic brain activity in real time and train individuals to actively control over their brain activation by using neurofeedback. Independent Component Analysis (ICA) is a data-driven method which can recover a set of independent sources from data without using any prior information. Since ICA was firstly proposed to be applied to fMRI data by Mckeown (1998), it has become more and more popular in offline fMRI data analysis. However, ICA was seldom used in real-time fMRI studies due to its large time cost. Although Esposito (2005) proposed a real-time ICA (rtICA) framework by combining FastICA with a sliding-window approach, it was only applied to analyze single-slice data rather than full-brain data and was not stable. The semi-blind rtICA (sb-rtICA) method proposed by Ma (2011) can reduce the computation time and improve stability by adding regularization of certain estimated time course using the experiment paradigm information to rtICA. However, the target independent component (IC) cannot be extracted as the first component in all sliding windows by sb-rtICA, which still adds computation time to some extent. The constrained ICA proposed by Lu (2005) can eliminate the ICA’s indeterminacy on permutation. In this study, we proposed a real-time Constrained Independent Component Analysis (rtCICA) method by combining CICA with the sliding-window technique to improve the performance of rtICA. The basic idea of rtCICA is to induce spatial prior information as constraints into ICA so that the target IC can be always automatically extracted as the first one. Both simulated and real-time fMRI experiments demonstrated that rtCICA outperforms rtICA greatly in the stability and the computational time.

Neuroimaging studies have revealed that motor imagery (MI) shared similar neural substrates with motor execution (ME) though there are some differences in the activation pattern. Most previous studies generally focused on voxel-wise based analysis. However, the congruence and difference in functional brain network relevant to MI and ME task has been rarely investigated. In this study, independent component analysis (ICA) was applied to characterize the functional brain networks underlying MI and ME. Results shows that the brain networks underlying MI and ME shared similar brain regions consisted of supplementary motor area (SMA), contralateral primary sensorimotor area (M1/S1), striatum, bilateral premotor area (PMA), posterior parietal lobule (PPL), and cerebellum. However, the ME task induced stronger activities in SMA-proper, bilateral M1/S1 and cerebellum while the MI task produced greater activities in preSMA, right cerebellum, bilateral PMA, parietal cortex and striatum. These findings are in accordance with the model proposed by Hikosaka (2002) that includes the parietal–prefrontal cortical loops for a spatial sequence and the motor cortical loops for a motor sequence. Moreover, the functional connectivity within the MI/ME-relevant network was evaluated using hierarchical integration that can quantify the total amount of interaction within the network and further assess the information exchanges within/between sub-networks. Results of hierarchical integration further indicate that parietalprefrontal areas contributes more to the integration of MI network than that of ME network while motor cortical areas contributes more to the integration of ME network than that of MI network.

Motor tasks, in our daily life, could be performed through execution and imagination. The brain response underlying these movements has been investigated by many studies. Neuroimaging studies have reported that both execution and imagination could activate several brain regions including supplementary motor area (SMA), premotor area (PMA), primary sensorimotor area (M1/S1), posterior parietal lobe (PPL), striatum, thalamus and cerebellum. These findings were based on the regional activation, and brain regions have been indicated to functionally interact with each other when performing tasks. Therefore further investigation in these brain regions with functional connectivity measurements may provide new insights into the neural mechanism of execution and imagination. As a fundamental measurement of functional connectivity, connection strength of graph theory has been used to identify the key nodes of connection and their strength-priorities. Thus, we performed a comparative investigation between execution and imagination tasks with functional magnetic resonance imaging (fMRI), and further explored the key nodes of connection and their strength-priorities based on the results of functional activations. Our results revealed that bilateral SMA, contralateral PMA, thalamus and M1/S1 were involved in both tasks as key nodes of connection. These nodes may play important roles in motor control and motor coordination during execution and imagination. Notably, the strength-priorities of contralateral PMA and thalamus were different between the two tasks. Higher strength-priority was detected in PMA for imagination, implicating that motor planning may be more involved in the imagination task.

Many studies have reported that discrete cortical areas in the ventral temporal cortex of humans were correlated with the perception of pictures visual stimuli. Moreover, event-related potentials caused by different kinds of picture stimuli showed different amplitude levels of N170 which was maximal over occipito-temporal electrode sites. However, the phenomenon which is mentioned above may be correlated with some local bold signal change, and where is the change happened is still unclear. Recently, research for EEG-fMRI has been widely performed through General Linear Model (GLM) to find the relationship between some feature of the ERP component and the activation of local brain area. In our study, we dealt with the simultaneously recorded EEG-fMRI data of picture stimuli to find the correlation between the change of the N170’s amplitude and the BOLD signal. The amplitudes of the N170 component from the average ERPs of 4 different kinds of picture stimuli were extracted from the EEG data and the activation map for the same stimuli was provided based on the fMRI data. GLM was performed including regressors that could represent the change of the N170’s amplitude. Our result showed that fusiform and occipital gyrus were activated by the parametric design and were overlapped by the activation map of the common fMRI design. Thus we might infer that these regions had relationship with the change of the amplitudes of N170. Our research may contribute to location of the source of N170 and bring a new approach for the parameter design of the fMRI signal in EEG-fMRI analysis.

Functional MRI is fundamentally grounded in the hemodynamic response. With an increase in neuronal activity, blood flow increases, causing an increase in blood oxygenation, leading to an increase in transverse relaxation rate T2*. This increase in blood flow is slow and highly variable and shows a considerable spatial heterogeneity. In spite of these limitations, the hemodynamic response has been proven to be exquisitely sensitive to subtle differences in neuronal activity in time, over space, and between subjects. This paper is a brief review of my Keynote address describing some of the effort coming from my group that further demonstrates methods to robustly extract ever more information from both resting state fMRI and activation-induced fMRI. Specifically, I discuss 1) our new method to use multi-echo fMRI time series data collection to separate blood oxygen level dependent (BOLD) signal from non-BOLD signal, 2) activation of the whole brain obtained using a simple task and, importantly massive averaging and a model-free analysis approach, and 3) fMRI decoding of left vs right eye ocular dominance column activation with a timing offset as low as 100 ms.

The recent discovery of methodological flaws in experimental design and analysis in neuroscience research has raised concerns over the validity of certain techniques used in routine analyses and their corresponding findings. Such concerns have centered around selection bias whereby data is inadvertently manipulated such that the resulting analysis produces falsely increased statistical significance, i.e. type I errors. This has been illustrated recently in flv1RI studies, with excessive flexibility in data collection, and general experimental design issues. Current work from our group has shown how this problem extends to generic voxel-based analysis (and certain technique derivatives such as tract- based spatial statistics) using fractional anisotropy images derived from diffusion tensor imaging. In this work, we demonstrate how this circularity principle can potentially extend to the well-known optimized voxel-based morphometry technique for assessing cortical density differences whereby the principal cause of experimental corruption is due to normalization strategy. Specifically, the popular sum­ of-squared-differences (SSD) metric explicitly optimizes statistical findings potentially inflating type I errors. Additional experimentation demonstrates that this problem is not restricted to the SSD metric but extends to other commonly used metrics such as mutual information, neighborhood cross correlation, and Demons.

The skull of young children is made up of bony plates that enable growth. Craniosynostosis is a birth defect that causes one or more sutures on an infant’s skull to close prematurely. Corrective surgery focuses on cranial and orbital rim shaping to return the skull to a more normal shape. Functional problems caused by craniosynostosis such as speech and motor delay can improve after surgical correction, but a post-surgical analysis of brain development in comparison with age-matched healthy controls is necessary to assess surgical outcome. Full brain segmentations obtained from pre- and post-operative computed tomography (CT) scans of 8 patients with single suture sagittal (n=5) and metopic (n=3), nonsyndromic craniosynostosis from 41 to 452 days-of-age were included in this study. Age-matched controls obtained via 4D acceleration-based regression of a cohort of 402 full brain segmentations from healthy controls magnetic resonance images (MRI) were also used for comparison (ages 38 to 825 days). 3D point-based models of patient and control cohorts were obtained using SPHARM-PDM shape analysis tool. From a full dataset of regressed shapes, 240 healthy regressed shapes between 30 and 588 days-of-age (time step = 2.34 days) were selected. Volumes and shape metrics were obtained for craniosynostosis and healthy age-matched subjects. Volumes and shape metrics in single suture craniosynostosis patients were larger than age-matched controls for pre- and post-surgery. The use of 3D shape and volumetric measurements show that brain growth is not normal in patients with single suture craniosynostosis.

Mesial-temporal lobe epilepsy (mTLE), a neurological disorder characterized by abnormal synchronous discharges in a large cell population, affects the hemodynamic activities of functional networks remote from the epileptogenic zone and causes widespread deficits in brain functions. Although a number of resting-state fMRI studies have found altered spatial patterns in the canonical resting-state networks (RSNs) in patients with mTLE, including the default mode network (DMN), dorsal lateral attention network (DAN), auditory network (AUN), somatosensory network (SMN) and visual network (VIN), none of these studies has addressed the question whether the frequencies of hemodynamic oscillations in these RSNs were altered. In the present study, we have proposed a network-based temporal clustering analysis (TCA) method to characterize the resting hemodynamic activity of a large-scale functional network. First, the RSNs were identified in healthy controls as well in the left mTLE patients using independent component analysis (ICA). Then, a time course representing the hemodynamic activity of each RSN was extracted by counting the number of the voxels that were activated simultaneously at each time point within the network. Finally, the power spectral density (PSD) of the time course was estimated. Our results have demonstrated significant differences in the frequency profiles of the SMN, VIN and left DAN between the patients and controls: the peaks of these spectra shifted toward a lower frequency in the patients, while more power was distributed over higher frequency bands in the healthy controls. However, no significant difference has been found in the AUN, DMN and right DAN. These features might serve as biomarkers to differentiate the patients from controls.

Mouse lung models facilitate the investigation of conditions such as chronic inflammation which are associated with common lung diseases. The multi-scale manifestation of lung inflammation prompted us to use multi-scale imaging - both in vivo, ex vivo MRI along with ex vivo histology, for its study in a new quantitative way. Some imaging modalities, such as MRI, are non-invasive and capture macroscopic features of the pathology, while others, e.g. ex vivo histology, depict detailed structures. Registering such multi-modal data to the same spatial coordinates will allow the construction of a comprehensive 3D model to enable the multi-scale study of diseases. Moreover, it may facilitate the identification and definition of quantitative of in vivo imaging signatures for diseases and pathologic processes. We introduce a quantitative, image analytic framework to integrate in vivo MR images of the entire mouse with ex vivo histology of the lung alone, using lung ex vivo MRI as conduit to facilitate their co-registration. In our framework, we first align the MR images by registering the in vivo and ex vivo MRI of the lung using an interactive rigid registration approach. Then we reconstruct the 3D volume of the ex vivo histological specimen by efficient group wise registration of the 2D slices. The resulting 3D histologic volume is subsequently registered to the MRI volumes by interactive rigid registration, directly to the ex vivo MRI, and implicitly to in vivo MRI. Qualitative evaluation of the registration framework was performed by comparing airway tree structures in ex vivo MRI and ex vivo histology where airways are visible and may be annotated. We present a use case for evaluation of our co-registration framework in the context of studying chronic inammation in a diseased mouse.

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

Volumetric computed tomography (CT) imaging provides a method of acquiring a 3-Dimensional view of lung soft tissue. The data captured in these images allows several methods of assessing the state of health of the lung. This information can prove valuable in early diagnosis of conditions where lung tissue is damaged, before external symptoms are expressed. The imaging data is also necessary for modeling lung tissue mechanics. This paper presents some analysis techniques for lung soft tissue, and uses these techniques to compare healthy lungs of young and elderly subjects.

The upper airways are a major site of congenital and acquired pediatric airway obstruction. Airway size information can be used for pre-surgical planning and post-treatment assessment. The aim of this research is to develop a greater understanding of the growth and variation of the normal pediatric airway to assist a surgeon in optimizing the outcomes for patients by increasing efficiency and accuracy. The standard imaging tool for measuring airway geometry has been CT (computed tomography), but to eliminate the risks of radiation exposure during prospective studies, we have developed an image analysis system to measure airway geometry in MR (magnetic resonance) images of the upper airway. Six adult patients that had CT and MR images of a normal airway were used as the training set to optimize the segmentation cost function to find the appropriate 3D surface. 25 normal pediatric subjects were segmented and measured and then compared to the segmentations of three experts. Dice similarity coefficient and boundary point distances were used for comparison metrics. The automated segmentations correlated well and were not significantly different from those from the group of experts. A group of 90 children were measured and plotted against age, gender, z-scores for weight and height. In an attempt to model growth, cross-sectional area showed a significant correlation with a 4^th degree polynomial of age. This work demonstrates that MR imaging can be used for measuring the pediatric upper airway and to develop growth models to assist in pre-surgical planning.

Small pulmonary vessel networks (arteriole and venule) provide a significant insight into understanding the alveolated structure in the human acinus. However, automatic extraction of small pulmonary vessels is a challenge due to the presence of abundant complexities in the networks. We thereby introduce a stochastic framework, a particle filter, to track small vessels running inside alveolar walls in human acinus using synchrotron radiation micro CT (SRμCT) images. We formulated vessel tracking using a non-linear sate space which captures both smoothness of the trajectories and intensity coherence along vessel orientations. In the particle filter scheme, we computed the proposal distribution by using the orientation distribution function (ODF), which is estimated as the combination of three different profiles; appearance, directional, and medialness profiles. To model the posterior distribution, we obtained voxels inside cylindrical tube which encapsulated a local vessel part. We constructed the prior distribution using the von Mises-Fisher (vMF) distribution on a unit sphere. At the same time, we detected branches of a vessel by analyzing the dominance of local vessel orientations through the vMF mean shift algorithm. Given a seed point, the method is able to locate the optimal vessel networks inside alveolar walls. Applying the method to the SRμCT images of the human lung acini, we demonstrate its potential usefulness to extract the trajectories of small pulmonary vessels running inside the alveolar walls.

The interaction between mechanical obstruction and outcome in pulmonary embolism (PE) is not well quantified. Therefore a simple prognostic tool that can be used quickly in the clinical setting remains elusive. Several scoring systems have been proposed to address this problem. However, they are unable to adequately capture the functional outcomes in PE so have not been adopted widely clinically. Here we present an image-based computational model that correlates very well with measures of RV dysfunction. The model extracts the geometric features of the lung, airways, blood vessels and emboli from CTPA (computed tomography pulmonary angiogram) imaging and simulates function (perfusion, ventilation and gas exchange) within these geometries. This results in subject-specific predictions of function in 9 patients with acute PE. There is a high correlation between model results and indicators of right heart dysfunction (p=0.001 in the case of the ratio between right and left ventricular volumes and p<0.03 in the case of systolic pulmonary artery pressure estimated from echocardiography). An existing scoring system that accounts only for the mechanical obstruction of capillary bed performs less well than the model (p=0.04 in the case of the ratio between right and left ventricular volumes and p=0.23 in the case of systolic pulmonary artery pressure estimated from echocardiography). This suggests that the functional impact of occlusion must be accounted to construct useful PE scoring systems.

Since many diseases or injuries can cause biomechanical or structural property changes that can alter lung function, there is great interest in measuring regional lung function by measurement of regional mechanical changes. To date, the most prevalent approach for assessing regional lung function from 4-D X-ray CT data has been a measure of Jacobian of deformation. However, although the Jacobian describes regional volume changes of the lung during deformation, it lacks any consideration of directional changes of local compressions and expansions during respiration. Herein, we propose the use of strain as a measure of regional lung function from 4-D thoracic CT and we perform correlation of principal strains of calculated deformation by s recently proposed 3-D optical flow technique (MOFID) computed from radiotherapy treatment planning 4-D X-ray CT data sets collected in seven subjects suffering from non-small cell primary lung cancer. In addition to 4-D CT data, both SPECT ventilation (V_SPECT), and SPECT perfusion (Q_SPECT) data were acquired in all subjects. For each subject, we performed voxel-wise statistical correlation of the Jacobian as well as principal strains of deformation (CT-derived pulmonary function images) with both ventilation and perfusion SPECT. For all subjects, the maximum principal strain resulted in a higher correlation with both SPECT ventilation and SPECT perfusion than other indices including the previously established Jacobian metric.

The six-stage Frisén scale is a qualitative and subjective method for assessing papilledema (optic disc swelling due to raised intracranial pressure) using fundus photographs. The recent introduction of spectral-domain optical coherence tomography (SD-OCT) presents a promising alternative to enable the 3-D quantitative estimation of papilledema. In this work, we propose an automated region-based volumetric estimation of the degree of papilledema from SD-OCT. After using a custom graph-based approach to segment the surfaces of the swollen optic nerve head, the volumes of the nasal, superior, temporal, and inferior regions are computed. Using a dataset of 70 SD-OCT optic-nerve-head (ONH) SD-OCT scans the Spearman rank correlation coefficients between expert-defined Frisén scale grades and the total retinal (TR) volume, nasal, superior, temporal, inferior regional volumes were 0.737, 0.752, 0.747, 0.770 and 0.758, respectively. Also, a fuzzy k-nearest-neighbor (k-NN) algorithm was used to predict Frisén scale grades (in a leave-one-subject-out fashion). Using multiple features rather than just the TR volume made the resulting mean Frisén grade difference (MGD) between the expert-defined grades 0.386 (down from 0.629) and prediction accuracy 64.29% (up from 41.43%).

Trans-aortic valve replacement is a new endovascular procedure which has started to be used routinely in cardiac interventional suites. During such procedures a stent-like device containing new aortic valves is placed over the damaged ones, possibly causing calcifications to be dislodged and released in arteries leading to stroke. To prevent such events, new devices are being developed to provide distal protection to the brain supplying arteries. Currently there is a need to evaluate such device efficacy in a repeatable manner. We are proposing and investigating such a method based on particle optical tracking. We simulated such protective devices using two porous screens (150 and 200 μm pore size) which were placed in an arterial bifurcation phantom connected to a clinically relevant flow loop. A mask was acquired and gold embolic particles (100-300μm) were injected at a steady rate using a motorized injector. Optical images with 2 ms exposure were acquired at 30 fps. Images were subtracted, thresholded and filtered using a 5x5 median filter. ROI’s were drawn over the main and bifurcating arteries and a particle counting algorithm was used to estimate particle flow rates in each artery for each run. The unprotected and the two protected cases were evaluated. Before filter placement, the particle flow rate was 60 and 40 %, respectively, of the main artery. After the filter placement, the particle flow rate in the protected branch was 4% and 8% of the particle flow rate in the main artery. We present a method to assess the efficacy of such devices using an optical particle tracking and counting technique.

Photodynamictherapy (PDT) uses a drug called a photosensitizer that is excited by irradiation with a laser light of a particular wavelength, which generates reactive singlet oxygen that damages the tumor cells. The photosensitizer and light are inert; therefore, systemic toxicities are minimized in PDT. The synthesis of novel PDT drugs and the use of nanosized carriers for photosensitizers may improve the efficiency of the therapy and the delivery of the drug. In this study, we formulated two nanoparticles with and without a targeting ligand to encapsulate phthalocyanines 4 (Pc 4) molecule and compared their biodistributions. Metastatic human head and neck cancer cells (M4e) were transplanted into nude mice. After 2-3 weeks, the mice were injected with Pc 4, Pc 4 encapsulated into surface coated iron oxide (IO-Pc 4), and IO-Pc 4 conjugated with a fibronectin-mimetic peptide (FMP-IO-Pc 4) which binds specifically to integrin β1. The mice were imaged using a multispectral camera. Using multispectral images, a library of spectral signatures was created and the signal per pixel of each tumor was calculated, in a grayscale representation of the unmixed signal of each drug. An enhanced biodistribution of nanoparticle encapsulated PDT drugs compared to non-formulated Pc 4 was observed. Furthermore, specific targeted nanoparticles encapsulated Pc 4 has a quicker delivery time and accumulation in tumor tissue than the non-targeted nanoparticles. The nanoparticle-encapsulated PDT drug can have a variety of potential applications in cancer imaging and treatment.

Magnetic Particle Imaging (MPI) has been presented by Gleich and Weizenecker in 2005. Since then, a number of innovations have been introduced by many di erent research groups. In 2009, for instance, Sattel et al. presented a novel single-sided MPI scanner geometry. The major advantage of this particular scanner geometry is the unlimited measurement eld. For the imaging process in MPI, super-paramagnetic iron oxide nanoparticles (SPIONs) are applied as tracer material. The tracer is excited by sinusoidally varying magnetic elds. In this contribution, simulated magnetic elds were evaluated based on the measured eld distribution of a single-sided scanner realization. It is of particular importance to know the quality of the gradient elds, since image resolution in MPI is directly linked to the gradient strength.

In Magnetic Particle Imaging the spatial distribution of superparamagnetic iron-oxide nanoparticles is determined using oscillating magnetic fields. The change of particle magnetization is recorded with receive coils. Spatial encoding is achieved with a superimposed gradient field featuring a field-free point. Particles not located in the vicinity of this point are in saturation and therefore do not induce a signal in the coils. Image reconstruction based on a system matrix is accurate, but time consuming. Recently, a method was introduced that images several small patches instead of one large field of view. This contribution applies this approach and additionally suggests to reusing the system matrix of one patch for the reconstruction of all patches. We will motivate this idea with symmetry characteristics of the magnetic fields applied in Magnetic Particle Imaging and perform a simulation study on homogeneous as well as inhomogeneous fields to show the potential of the approach.

Magnetic particle imaging (MPI) is a promising new imaging method capable of determining the spatial distribution of magnetic nanoparticle tracers in real-time. By means of a time-varying magnetic field the non-linear response of the nanoparticle tracer is observed. Under constraints of an additional gradient field with a field field point (FFP) an image is reconstructed from the resulting spectrum. Our magnetic particle imaging scanner covers a field of view of about 22×22×15 mm³. It features a bore size of 30 mm, large enough to fit a ventilated mouse under laboratory conditions. Imaging plastic or bio-compatible phantoms, we were studying the properties of the MPI system function and its dependence on imaging parameters. It is shown that the required reference scan can be significantly simplified and that an improvement in image quality can be achieved by the use of a hybrid approach with model-based and sampled data points for the system matrix.

Nanoparticles have a long history of successful application to medical technologies. Many of these technologies also employ the magnetic properties of some particles. Thus, an increased understanding of the dynamic properties of magnetic particles in time-varying magnetic fields is essential for advancement in sensing, counting, imaging or therapeutic modalities. A stochastic Langevin equation approach to particle modeling has been documented previously, however this new study focuses on comparison of the model to other theoretical modeling approaches as well as current experimental techniques from magnetic nanoparticle spectroscopy. The results show that the model works for a larger bandwidth than many separate approximate methods, and that the harmonics of the magnetization found through simulation contain enough information to infer microenvironmental parameters, therefore justifying spectroscopic usage.

Superparamagnetic iron oxide (SPIO) nanoparticles have been produced and used successfully as potent contrast agents for Magnetic Resonance Imaging (MRI). However, a significant challenge associated with the biological application of SPIO-tracer agents is their behavior in vivo since their efficacy is often compromised due to a rapid recognition and clearance by the reticuloendothelial system (RES) which limits the applicability of such compounds in MRI. The advances in nanotechnology and molecular cell biology had lead to improve stability and biocompatibility of these nanoparticles, but despite a number of efforts, the SPIO half-life in blood circulation is very short. In this contest, the potential of red blood cells (RBCs) loaded with SPIO nanoparticles as a tracer material for MRI has been investigated in order to realize a blood pool tracer with longer blood retention time. Previously, we have proposed the encapsulation into RBCs of superparamagnetic iron oxide nanoparticles carboxydextran coated, such as Resovist contrast agent. This approach led to a nanoparticle reduction in uptake by the RES, increasing the blood circulation half-life of nanoparticles. Recently, the loading procedure was applied to a new contrast agent, the P904 ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles coated by hydrophilic derivatives of glucose, recently developed by Guerbet Laboratories. The results evidenced that this nanomaterial can be efficiently loaded into human and murine RBCs at concentrations ranging from 1.5 to 12 mM Fe. In vivo experiments performed in mice have showed an increased survival in the mouse vascular system of P904 encapsulated into RBCs respect to free P904 sample intravenously injected at the equivalent amounts.

We have applied an optical radiance technique to map localized inclusions of gold nanoparticles in a porcine phantom. Our goal was to show that combined spectroscopic and angular snapshots of phantoms allow the obtaining of information that is relevant for prostate cancer diagnostics. A combination of the radiance spectroscopy and white light spectroscopy was used to measure angular resolved light distribution in 600-900 nm spectral range inside the porcine phantom that mimics prostate geometry. Optical radiance defines a variation in the angular density of photons impinging on a selected point in the tissue from various directions. To obtain radiance data, a specially constructed optical probe with a well-defined angular detection window must be rotated along its axis. Characteristic spectro-angular snapshots of the phantom alone and with the localized inclusion of gold nanoparticles were obtained. The inclusions were formed by immersing a capillary filled with gold nanoparticles into selected locations in the phantom. For phantoms with gold inclusions, this approach allows the isolation of the spectroscopic signatures of the inclusions from the background and identification of inclusion locations in the angular domain. Detection of ~10¹⁰ gold nanoparticles (detector-inclusion separation 10 mm, source-detector separation 15 mm) in the porcine tissue was demonstrated. These encouraging results indicate a promising potential of radiance spectroscopy in early prostate cancer diagnostics with gold nanoparticles.

Unlike many other experimental imaging methods, elastography has enjoyed a strong link to the standard diagnostic and interventional evaluation technique of soft tissue palpation. As a result, the initial excitement about elastography quickly translated to clinical use (e.g., [1-3]) which now includes commercially available ultrasound and magnetic resonance (MR) elastography products. However, despite these advances, understanding what these macroscopic clinical-scale tissue measurements indicate with respect to the underlying cellular and tissue-matrix scale phenomena is largely unclear. In this work, we present preliminary data towards a more systematic study of the elasticity biomarker in characterizing cancer for therapeutic design and monitoring. In addition, we demonstrate that we can conduct these studies with techniques that are consistent across both pre-clinical (i.e., mouse) and clinical length scales. The elastography method we use is called modality independent elastography (MIE) [4, 5] and can be described as a highly translatable model-based inverse image-analysis method that reconstructs elasticity images using two acquired image volumes in a pre-post state of deformation. Quantitative phantom results using independent testing methods report an elastic property contrast between the inclusion and background as a 14.9 to 1 stiffness ratio with MIE reconstructing the ratio as 13.1 to 1. Preliminary elasticity reconstructions in murine and human systems are reported and are consistent with literature findings.

There is currently a paucity of reliable techniques for predicting the response of breast tumors to neoadjuvant chemotherapy. The standard approach is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging, but these methods generally do not show whether a tumor is responding until the patient has completed therapy. One promising approach to address this clinical need is to integrate quantitative in vivo imaging data into biomathematical models of tumor growth in order to predict eventual response based on early measurements during therapy. Contrast enhanced and diffusion weighted magnetic resonance imaging data acquired before and after the first cycle of therapy to calibrate a patient-specific response model can be used to predict patient outcome at the conclusion of therapy. We have developed a mathematical modeling approach to optimize key model parameters for the calibration of a patient-specific mechanically coupled reaction-diffusion model of response. We apply the approach to patient data in which tumors were either responsive or non-responsive to neoajuvant chemotherapy and demonstrate changes to the patient-specific model which result in altered growth patterns. Additionally, we show that reconstructed parameter maps exhibit drastic differences between patients with different tumor burden outcomes at the conclusion of therapy, in this case, a 10-fold increase in proliferative capacity is found for a non-responding tumor versus its responsive counterpart. Finally, we show that the mechanically coupled reaction-diffusion growth model, when projected forward, more accurately predicts residual tumor burden than the uncoupled model.

Magnetic Resonance Elastography (MRE) is an MRI-based technique that is used for the clinical diagnosis and staging of liver fibrosis by quantitatively measuring the stiffness of the liver. Due to the complexity of the signal characteristics and the presence of artifacts both in the acquired images and in the resulting stiffness images, the selection of the ROI for the stiffness measurement is currently performed manually, which may lead to significant inter- and intrareader variability. An algorithm has been developed to fully automate this analysis for liver MRE images. Automated segmentation of liver MRE images is challenging due to signal inhomogeneity, low contrast, and variability in patient anatomy. An initial liver contour is found by fitting Gaussian peaks to the image histogram and selecting the peak that comprises intensities in the expected range and produces a mask near the expected location of the liver. After correction to reduce intensity inhomogeneity, an active contour based on intensity, with morphology used to implicitly enforce smoothness, is used to segment liver tissue while avoiding blood vessels. The resulting mask is used to initialize another segmentation which splits the region of the elastogram belonging to the liver into homogeneous liver tissue and areas with inclusions, partial volume effects, and artifacts. In a set of 88 cases the algorithm had a -6.0 ± 14.2% stiffness difference from an experienced reader, which was superior to the 6.8 ± 22.8% difference between two readers. The segmentation was run on an additional 200 cases and the final ROIs were subjectively rated by a radiologist. The ROIs in 98% of cases received an average rating of “good” or “acceptable.”

The current approach to evaluating cartilage degeneration at the knee joint requires visualization of the joint space on radiographic images where indirect cues such as joint space narrowing serve as markers for osteoarthritis. A recent novel approach to visualizing the knee cartilage matrix using phase contrast imaging (PCI) with computed tomography (CT) was shown to allow direct examination of chondrocyte patterns and their subsequent correlation to osteoarthritis. This study aims to characterize chondrocyte cell patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage through texture analysis. Statistical features derived from gray-level co-occurrence matrices (GLCM) and geometric features derived from the Scaling Index Method (SIM) were extracted from 404 regions of interest (ROI) annotated on PCI images of healthy and osteoarthritic specimens of knee cartilage. These texture features were then used in a machine learning task to classify ROIs as healthy or osteoarthritic. A fuzzy k-nearest neighbor classifier was used and its performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM and GLCM correlation features. With the experimental conditions used in this study, both SIM and GLCM achieved a high classification performance (AUC value of 0.98) in the task of distinguishing between healthy and osteoarthritic ROIs. These results show that such quantitative analysis of chondrocyte patterns in the knee cartilage matrix can distinguish between healthy and osteoarthritic tissue with high accuracy.

Estimating local trabecular bone quality for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic hip fracture risk. In this study, we explore the ability of geometric features derived from the Scaling Index Method (SIM) in predicting the biomechanical strength of proximal femur specimens as visualized on multi-detector computed tomography (MDCT) images. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the non-linear micro-structure of the trabecular bone was characterized by statistical moments of its BMD distribution and by local scaling properties derived from SIM. Linear multi-regression analysis and support vector regression with a linear kernel (SVRlin) were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the FL values determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test set. The best prediction result was obtained from the SIM feature set with SVRlin, which had the lowest prediction error (RMSE = 0.842 ± 0.209) and which was significantly lower than the conventionally used mean BMD (RMSE = 1.103 ± 0.262, , p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens on MDCT images by using high-dimensional geometric features derived from SIM with support vector regression.

Osteogenesis imperfecta (OI) is a genetic disorder leading to increased bone fragility. Recent work has shown that the hierarchical structure of bone plays an important role in determining its mechanical properties and resistance to fracture. The current study represents one of the first attempts to characterize the 3D structure and composition of cortical bone in OI at the micron-scale. A total of 26 pediatric bone fragments from 18 individuals were collected during autopsy (N_c=5) or routing orthopaedic procedures (N_OI=13) and imaged by microtomography with a synchrotron light source (SRμCT) for several microstructural parameters including cortical porosity (Ca.V/TV), canal surface to tissue volume (Ca.S/TV), canal diameter (Ca.Dm), canal separation (Ca.Sp), canal connectivity density (Ca.ConnD), and volumetric tissue mineral density (TMD). Results indicated significant differences in all imaging parameters between pediatric controls and OI tissue, with OI bone showing drastically increased cortical porosity, canal diameter, and connectivity. Preliminary mechanical testing revealed a possible link between cortical porosity and strength. Together these results suggest that the pore network in OI contributes greatly to its reduced mechanical properties.

Rotator cuff disease is a degenerative disorder that is a common, costly, and often debilitating, ranging in severity from partial thickness tear, which may cause pain, to total rupture, leading to loss in function. Currently, clinical diagnosis and determination of disease extent relies primarily on subjective assessment of pain, range of motion, and possibly X-ray or ultrasound images. The final treatment plan however is at the discretion of the clinician, who often bases their decision on personal experiences, and not quantitative standards. The use of ultrasound for the assessment of tissue biomechanics is established, such as in ultrasound elastography, where soft tissue biomechanics are measured. Few studies have investigated the use of ultrasound elastography in the characterization of musculoskeletal biomechanics. To assess tissue biomechanics we have developed a device, which measures the force applied to the underlying musculotendentious tissue while simultaneously obtaining the related ultrasound images. In this work, the musculotendinous region of the infraspinatus of twenty asymptomatic male organized baseball players was examined to access the variability in tissue properties within a single patient and across a normal population. Elastic moduli at percent strains less than 15 were significantly different than those above 15 percent strain within the normal population. No significant difference in tissue properties was demonstrated within a single patient. This analysis demonstrated elastic moduli are variable across individuals and incidence. Therefore threshold elastic moduli will likely be a function of variation in local-tissue moduli as opposed to a specific global value.

Improving the appearance of the trunk is an important goal of scoliosis surgical treatment, mainly in patients' eyes. Unfortunately, existing methods for assessing postoperative trunk appearance are rather subjective as they rely on a qualitative evaluation of the trunk shape. In this paper, an objective method is proposed to quantify the changes in trunk shape after surgery. Using a non-invasive optical system, the whole trunk surface is acquired and reconstructed in 3D. Trunk shape is described by two functional measurements spanning the trunk length: the lateral deviation and the axial rotation. To measure the pre and postoperative differences, a correction rate is computed for both measurements. On a cohort of 36 scoliosis patients with the same spinal curve type who underwent the same surgical approach, surgery achieved a very good correction of the lateral trunk deviation (median correction of 76%) and a poor to moderate correction of the back axial rotation (median correction of 19%). These results demonstrate that after surgery, patients are still confronted with residual trunk deformity, mainly a persisting hump on the back. That can be explained by the fact that current scoliosis assessment and treatment planning are based solely on radiographic measures of the spinal deformity and do not take trunk deformity into consideration. It is believed that with our novel quantitative trunk shape descriptor, clinicians and surgeons can now objectively assess trunk deformity and postoperative shape and propose new treatment strategies that could better address patients' concern about their appearance.

Prostate cancer detection at early stage is very critical for desirable treatment outcome. In fact prostate cancer can be cured, if it is detected at early stage. Among imaging modalities used for cancer assessment, ultrasound elastography is emerging as an effective clinical tool for prostate and breast cancer diagnosis. Current clinical ultrasound elastography systems utilize strain imaging where tissue strain images are generated to approximate the tissue elastic modulus distribution. While strain images can be generated in real-time fashion, they lack the accuracy necessary for having high sensitivity and specificity. To improve strain imaging, researchers have developed full inversion based elastography techniques. These techniques are not based on simplifying assumptions such as tissue stress uniformity leading to accurate elastic modulus reconstruction. The drawback of these techniques, however, is that they are computationally intensive, hence are not suitable for real-time imaging. Among these techniques, a constrained elastography technique was developed which showed promising results as long as the tumor geometry can be obtained accurately from the imaging modality used in conjunction with elastography. This requirement is not easy to fulfill, especially with ultrasound imaging. To address this issue, we present an unconstrained full inversion ultrasound elastography method for prostate cancer imaging where knowledge of tissue geometry is not necessary. Tissue elastic modulus reconstruction in the proposed elastography technique is iterative, where each iteration involves tissue stress computation using Finite Element Method (FEM) followed by Young’s modulus updating using Hooke’s law. The method was validated using in silico and tissue mimicking prostate phantom studies. Results obtained from these studies indicate that the technique is reasonably accurate and robust.

Magnetic resonance elastography is a technique where mechanical properties of materials are estimated by fitting a mechanical model to an MRI-acquired displacement field. These models have been primarily limited to viscoelasticity and linear elasticity, and only recently has poroelasticity been utilized as an applied model. To validate these estimates, the same material is measured via an independent dynamic mechanical analysis device. However, these devices only apply analytic viscoelastic models. In some cases, there is a model mismatch if a viscoelastic mechanical analysis is being compared to a poroelastic model in elastography. Thus, a poroelastic dynamic mechanical analysis technique is needed to properly measure porous media and compare the results with the appropriate elastography technique. A finite element technique was implemented on a TA-Q800 Dynamic Mechanical Analysis machine similar to the algorithm used in the corresponding MR elastography method. A viscoelastic version of the finite element code was created to validate the theory and show results similar to those obtained by the analytic DMA solution. Also, differences were seen that can be attributed to inertial forces not accounted for by an analytical solution. A poroelastic algorithm was then applied, showing great promise in the ability to measure properties of porous tissues.

In studying Obstructive Sleep Apnea Syndrome (OSAS) in obese children, the quantification of obesity through MRI has been shown to be useful. For large-scale studies, interactive or manual segmentation strategies become inadequate. Our goal is to automate this process to facilitate high throughput, precision, and accuracy and to eliminate subjectivity in quantification. In this paper, we demonstrate the adaptation, to this application, of a general body-wide Automatic Anatomy Recognition (AAR) system that is being developed separately. The AAR system has been developed based on existing clinical CT image data of 50-60 year-old male subjects and using fuzzy models of a large number of objects in each body region. The individual objects and their models are arranged in a hierarchy that is specific to each body region. In the application under consideration in this paper, we are primarily interested in only the skin boundary, and subcutaneous and visceral adipose region. Further, the image modality is MRI, and the study subjects are 8-17 year-old females. We demonstrate in this paper that, once such a full AAR system is built, it can be easily adapted to a new application by specifying the objects of interest, their hierarchy, and a few other application-specific parameters. Our tests based on MRI of 14 obese subjects indicate a recognition accuracy of about 2 voxels or better for both types of adipose regions. This seems quite adequate in terms of the initialization of model-based graph-cut (GC) and iterative relative fuzzy connectedness (IRFC) algorithms implemented in our AAR system for subsequent delineation of the objects. Both algorithms achieved low false positive volume fraction (FPVF) and high true positive volume fraction (TPVF), with IRFC performing better than GC.

The purpose of this study is to analyze the effect of site-specific tau phosphorylation on cellular microfilaments networks. We examined cell images to study tau’s interaction with microfilaments in both wild type full-length (2N4R) tau and pathological human tau (PH-tau) when expressed in Chinese hamster ovarian fibroblasts (CHO). A custom ImageJ plugin was developed to provide quantitative analysis of the immunofluorescently labeled polymerized actin in cells expressing either of the above mentioned tau vectors. Using histograms of the pixel intensities of images, with userdefined thresholds, the code calculates the integrated densities and creates an output image to visualize the considered areas (those outside the thresholds are displayed as well). The data demonstrated the presence of an inverse correlation between the level of PH-tau expressed and the amount of total actin polymerization. Additionally, actin polymerization was not only interrupted by the presence of PH-tau but also punctate staining was also detected (as opposed to the normal fibril structure). These observations were not detected in cells expressing wild type tau. The visualization helped reveal some image acquisition anomalies such as varying levels of fluorescent staining as well as standardized image collection. The results should aid in a further understanding the mechanism of cellular degeneration induced by the hyperphosphorylation of MAP tau. Keywords: Microfilaments, Medical

Multi-voxel pattern analysis (MVPA) has been widely used in the object category classification of functional magnetic resonance imaging (fMRI) data. Feature selection is an essential operation in pattern classification. Searchlight, based on information mapping, is one method of feature selection. In contrast with traditional methods based on activation, searchlight has more sensitivity and then provides higher statistical power. In this study, we applied two different feature selection methods, searchlight and activation, combined with linear support vector machine (SVM) classifier, to investigate the classification effect in classifying 4-category objects on fMRI data. We found that the average classification accuracies of searchlight were 0.8095 (house vs. face), 0.7240 (house vs. car), 0.7247 (house vs. cat), 0.6980 (face vs. car), 0.5982 (face vs. cat) and 0.6860 (car vs. cat). For house vs. car, the average classification accuracy based on searchlight was better than that based on activation (0.7240 vs. 0.7143). Specially, searchlight method performed better than activation for some subjects. The results showed that object category classification of fMRI data based on information mapping were significantly reliable. Our findings suggest that information mapping can be applied in pattern classification in future work.

Multi-contrast MRI is a frequently used imaging technique in preclinical brain imaging. In longitudinal cross-sectional studies exploring and browsing through this high-throughput, heterogeneous data can become a very demanding task. The goal of this work was to build an intuitive and easy to use, dedicated visualization and side-by-side exploration tool for heterogeneous, co-registered multi-contrast, follow-up cross-sectional MRI data. The deformation field, which results from the registration step, was used to automatically link the same voxel in the displayed datasets of interest. Its determinant of the Jacobian (detJac) was used for a faster and more accurate visual assessment and comparison of brain deformation between the follow-up scans. This was combined with an efficient data management scheme. We investigated the functionality and the utility of our tool in the neuroimaging research field by means of a case study evaluation with three experienced domain scientists, using longitudinal, cross-sectional multi-contrast MRI rat brain data. Based on the performed case study evaluation we can conclude that the proposed tool improves the visual assessment of high-throughput cross-sectional, multi-contrast, follow-up data and can further assist in guiding quantitative studies.

Perfusion computed tomography (CT) has been widely used to assess the response of lung cancer treatment. However, the respiratory motion has become the major obstacle to the pixel-based time-series analyses. To minimize the effect of respiratory motion and investigate the feasibility of perfusion CT for prediction of tumor response and prognosis of non-small cell lung cancer, an image registration framework is proposed by unifying a virtual 3D local rigid alignment and 3D global non-rigid alignment. The basic idea is to use the perfusion CT data and routine whole-lung CT data, respectively. To realize this idea, maximum intensity projection (MIP) of the time series perfusion CT images is first generated, followed by decomposing the MIP image into region of interest (ROI), which is located on a lung nodule. For the ROI, affine transformation model based on mutual information is performed to estimate the virtual three dimensional linear deformations. Following that, the 3D thin plate spline (TPS) is carried out to establish the pixel correspondence between the paired volumetric CT data. The control points for the TPS are global feature points chosen from the boundary of whole lung, which are automatically derived by using the iterative closest point (ICP) matching Algorithm. The proposed algorithm has been evaluated both qualitatively and quantitatively on real lung perfusion CT datasets. From the time-intensity curves and perfusion parameters, the experiment results suggest that the findings on perfusion CT images obtained after treatment may be considered as a significant predictor of lung cancer.

Acute ischemic strokes are a major cause for death and severe neurologic deficits in the western hemisphere. The prediction of tissue outcome in case of an acute ischemic stroke is an important variable for treatment decision. An estimation of the expected outcome is typically obtained by thresholding a single perfusion parameter map, which is calculated from a perfusion CT dataset. However, cerebral perfusion is complex and the severity of perfusion impairment is not consistent within the penumbra of an acute ischemic stroke. Therefore, the application of only one parameter for acute stroke tissue outcome prediction may oversimplify the given problem. The aim of this study was to develop and evaluate the feasibility of a multiparametric approach for estimating tissue outcome in acute ischemic stroke patients using 15 CT perfusion datasets. For this purpose, perfusion parameter maps of cerebral blood flow, cerebral blood volume and mean transit time were calculated based on the concentration time curves derived from perfusion CT datasets. The parameter maps of ten patients were employed for a voxel-wise training of a support vector machine using ground-truth final infarct segmentations, whereas the remaining five patient datasets were used for evaluation of the voxel-wise prediction of tissue outcome using the trained support vector machine. Furthermore, tissue outcome was also predicted by optimal thresholding of corresponding time-to-peak (TTP) maps for comparison purposes. Both predictions were compared to ground-truth final infarct lesions for the five datasets used for evaluation. The proposed multiparametric tissue outcome prediction lead to superior prediction results in all cases. More precisely, the multiparametric prediction lead to a mean Dice coefficient of 0.556, while optimal thresholding of TTP maps lead to an average Dice-coefficient of 0.444 compared to the ground-truth infarct lesions. In conclusion, the evaluation results of the proposed method suggest that a multiparametric tissue outcome prediction may be feasible for CT perfusion datasets but needs to be evaluated in more detail.

Wall shear stress plays an important role in the development of cerebrovascular pathologies. Its impact on aneurysm initiation, progress and rupture, was reported in previous works during the last years. However, there is still no wide agreement about what WSS characteristics are responsible for triggering those biomechanical processes. The accuracy of the simulations has been successfully validated in the past. Although the incorporation of the blood rheology in large arterial systems containing aneurysms resulted in similar hemodynamic characterizations for most aneurysms, large aneurysms, especially those containing blebs, are expected to have flow rates in the range where Newtonian and non-Newtonian models largely differ. However, there is no consent among authors about the impact of blood rheology on the intraaneurysmal WSS magnitude. In this work we used high resolution models reconstructed from rotational angiography images to perform unsteady finite element blood flow simulations to investigate the differences in WSS distribution and alignment for Newtonian and non-Newtonian rheologies. Unstructured finite element meshes were generated using an advancing front technique. Flow rate wave form was imposed at the inlets after scaling according to the Murray's Law for optimal arterial networks. The Casson model was incorporated as a velocity-dependent apparent viscosity and the results were compared to those using the Newtonian rheology. Associations between the localization of regions with large differences in wall shear stress magnitude and orientation, and the regions of differentiated wall shear stress magnitude were studied in a cohort of patients.

It is widely accepted that wall shear stress is associated to aneurysm formation, growth and rupture. Early identification of potential risk factors may contribute to decide the treatment and improve patient care. Previous studies have shown associations between high aneurysm wall shear stress values and both elevated risk of rupture and regions of aneurysm growing. Based on the assumption that damaged regions of the endothelium have different mechanical properties, regions with differentiated wall displacement amplitudes are expected. A previous approach based on the analysis of bidimensional dynamic tomographic angiography had been designed to investigate those correlations, but its main limitation was that wall motion was measured in a selected plane. The purpose of this work is to overcome some of those limitations. High time and spatial resolution 4D computerized tomographic angiography images of cerebral aneurysms were acquired and analyzed in order to identify and characterize wall motion. Images were filtered and segmented at nineteen time points during the cardiac cycle and displacement was estimated within the aneurysm sac and compared to wall shear stress distributions from patient-specific unsteady finite element blood flow simulations.

Genetically engineered mouse models of lung cancer are essential for preclinical evaluation of disease progression and treatments as well as in drug development. Micro-computed Tomography (microCT) is an imaging modality that is widely used in visualizing the anatomy of subjects in vivo and extracting quantitative and translatable biomarkers. This work demonstrates the use of uCT imaging and image segmentation techniques in large population phenotyping studies of transgenic mouse models of lung cancer. We studied 8 genotypes of transgenic mice with 99 subjects imaged at 4 time points. We developed (1) a high throughput image acquisition technique that acquires 60 subjects in 3 hours at an isotropic resolution of about 100 um, and (2) an automated segmentation algorithm to compute tumor and vasculature volume (TVV), a previously validated biomarker for lung cancer progression. TVV is computed as the difference between the whole lung and the functional lung (air space within lung) volumes. Previous work on automated lung segmentation focused on healthy lung or on segmentation of pulmonary nodules. We automatically compute TVV by determining a lung region of interest (ROI) by using the rib cage, the functional lung volume by thresholding within the lung ROI, and the whole lung volume by iteratively performing morphological hole-fill, bridge, and image close operations on the functional lung. We compare the automated results with that of manual analysis. Automated functional lung volume results were highly correlated to manual results (R²≥0.95) at all the time points. Whole lung volume was well-correlated to manual measurements (R²≥0.8 up to the 2^nd time point), but required some manual correction at later time points when the tumors almost filled the lung. Overall this approach provided about 66% time saving compared to manual analysis. Our innovative workflow with high throughput acquisition and automated segmentation enabled efficient phenotyping studies to aid drug development.

Monkeypox virus is an emerging zoonotic pathogen that results in up to 10% mortality in humans. Knowledge of clinical manifestations and temporal progression of monkeypox disease is limited to data collected from rare outbreaks in remote regions of Central and West Africa. Clinical observations show that monkeypox infection resembles variola infection. Given the limited capability to study monkeypox disease in humans, characterization of the disease in animal models is required. A previous work focused on the identification of inflammatory patterns using PET/CT image modality in two non-human primates previously inoculated with the virus. In this work we extended techniques used in computer-aided detection of lung tumors to identify inflammatory lesions from monkeypox virus infection and their progression using CT images. Accurate estimation of partial volumes of lung lesions via segmentation is difficult because of poor discrimination between blood vessels, diseased regions, and outer structures. We used hard C-means algorithm in conjunction with landmark based registration to estimate the extent of monkeypox virus induced disease before inoculation and after disease progression. Automated estimation is in close agreement with manual segmentation.

Early diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging based biomarkers of the disease that reflect the microstructural characteristics of neuronal pathways in the brain. One of the most common symptoms is reduced language development. We quantify this reduction using a graph-based large-scale network analysis of the connectome with a focus on the language related areas of the brain. Using a group of 18 children suffering from ASD and 18 typically developed controls we show that the reduced capacity for comprehension of language in ASD is reflected in the significantly (p < 0.001) reduced network centrality of Wernicke's area while the motor cortex, that was used as a control region, did not show any significant alterations. These results suggest Wernicke's area is less well integrated within the brain connectome in children suffering from ASD.