Denoising algorithms are sensitive to the noise level and noise power spectrum of the input image and their ability to adapt to this. In the worst-case, image structures can be accidentally removed or even added. This holds up for analytical image filters but even more for deep learning-based denoising algorithms due to their high parameter space and their data-driven nature. We propose to use the knowledge about the noise distribution of the image at hand to limit the influence and ability of denoising algorithms to a known and plausible range. Specifically, we can use the physical knowledge of X-ray radiography by considering the Poisson noise distribution and the noise power spectrum of the detector. Through this approach, we can limit the change of the acquired signal by the denoising algorithm to the expected noise range, and therefore prevent the removal or hallucination of small relevant structures. The presented method allows to use denoising algorithms and especially deep learning-based methods in a controlled and safe fashion in medical x-ray imaging.
KEYWORDS: Denoising, Breast, Education and training, Digital breast tomosynthesis, Tomosynthesis, Computer simulations, Deep learning, X-rays, Breast density, Photons
PurposeHigh noise levels due to low X-ray dose are a challenge in digital breast tomosynthesis (DBT) reconstruction. Deep learning algorithms show promise in reducing this noise. However, these algorithms can be complex and biased toward certain patient groups if the training data are not representative. It is important to thoroughly evaluate deep learning-based denoising algorithms before they are applied in the medical field to ensure their effectiveness and fairness. In this work, we present a deep learning-based denoising algorithm and examine potential biases with respect to breast density, thickness, and noise level.ApproachWe use physics-driven data augmentation to generate low-dose images from full field digital mammography and train an encoder-decoder network. The rectified linear unit (ReLU)-loss, specifically designed for mammographic denoising, is utilized as the objective function. To evaluate our algorithm for potential biases, we tested it on both clinical and simulated data generated with the virtual imaging clinical trial for regulatory evaluation pipeline. Simulated data allowed us to generate X-ray dose distributions not present in clinical data, enabling us to separate the influence of breast types and X-ray dose on the denoising performance.ResultsOur results show that the denoising performance is proportional to the noise level. We found a bias toward certain breast groups on simulated data; however, on clinical data, our algorithm denoises different breast types equally well with respect to structural similarity index.ConclusionsWe propose a robust deep learning-based denoising algorithm that reduces DBT projection noise levels and subject it to an extensive test that provides information about its strengths and weaknesses.
KEYWORDS: Medical image reconstruction, Bone, X-ray computed tomography, Sensors, X-rays, Medical imaging, Aluminum, Physics, Photons, Signal attenuation, Monte Carlo methods
We investigate the feasibility of bone marrow edema (BME) detection using a kV-switching Dual-Energy (DE) Cone-Beam CT (CBCT) protocol. This task is challenging due to unmatched X-ray paths in the low-energy (LE) and high-energy (HE) spectral channels, CBCT non-idealities such as X-ray scatter, and narrow spectral separation between fat (bone marrow) and water (BME). We propose a comprehensive DE decomposition framework consisting of projection interpolation onto matching LE and HE view angles, fast Monte Carlo scatter correction with low number of tracked photons and Gaussian denoising, and two-stage three-material decompositions involving two-material (fat-Aluminum) Projection-Domain Decomposition (PDD) followed by image-domain three-material (fat-water-bone) base-change. Performance in BME detection was evaluated in simulations and experiments emulating a kV-switching CBCT wrist imaging protocol on a robotic x-ray system with 60 kV LE beam, 120 kV HE beam, and 0.5° angular shift between the LE and HE views. Cubic B-spline interpolation was found to be adequate to resample HE and LE projections of a wrist onto common view angles required by PDD. The DE decomposition maintained acceptable BME detection specificity (⪅0.2 mL erroneously detected BME volume compared to 0.85 mL true BME volume) over +/-10% range of scatter magnitude errors, as long as the scatter shape was estimated without major distortions. Physical test bench experiments demonstrated successful discrimination of ~20% change in fat concentrations in trabecular bone-mimicking solutions of varying water and fat content.
Purpose: We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition. Methods: A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm2 dual-layer FPD (top layer: 0.20 mm CsI:Tl, bottom layer: 0.55 mm CsI:Tl; a 1 mm Cu filter between the layers to improve spectral separation). Tube settings were 120 kV (+2 mm Al, +0.2 mm Cu) and 10 mAs per exposure. The digital phantom consisted of a 160 mm water cylinder with inserts containing mixtures of water (volume fraction ranging 0.18 to 0.46) - fat (0.5 to 0.7) - Ca (0.04 to 0.12); decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material DE decomposition was applied to DE CBCT projections: first, projection-domain decomposition (PDD) into fat-aluminum basis, followed by CBCT reconstruction of intermediate base images, followed by image-domain change of basis into fat, water and bone. Sensitivity to scatter was evaluated by i) adjusting source collimation (12 to 400 mm width) and ii) subtracting various fractions of the true scatter from the projections at 400 mm collimation. The impact of spectral calibration was studied by shifting the effective beam energy (± 2 keV) when creating the PDD lookup table. We further simulated a realistic BME imaging framework, where the scatter was estimated using a fast Monte Carlo (MC) simulation from a preliminary decomposition of the object; the object was a realistic wrist phantom with an 0.85 mL BME stimulus in the radius. Results: The decomposition is sensitive to scatter: approx. <20 mm collimation width or <10% error of scatter correction in a full field-of-view setting is needed to resolve BME. A mismatch in PDD decomposition calibration of ± 1 keV results in ~25% error in fat fraction estimates. In the wrist phantom study with MC scatter corrections, we were able to achieve ~0.79 mL true positive and ~0.06 mL false positive BME detection (compared to 0.85 mL true BME volume). Conclusions: Detection of BME using DE CBCT with dual-layer FPD is feasible, but requires scatter mitigation, accurate scatter estimation, and robust spectral calibration.
KEYWORDS: Denoising, X-rays, Digital breast tomosynthesis, X-ray imaging, Photons, Mammography, Sensors, Physics, Signal to noise ratio, Interference (communication)
Digital Breast Tomosynthesis (DBT) is becoming increasingly popular for breast cancer screening because of its high depth resolution. It uses a set of low-dose x-ray images called raw projections to reconstruct an arbitrary number of planes. These are typically used in further processing steps like backprojection to generate DBT slices or synthetic mammography images. Because of their low x-ray dose, a high amount of noise is present in the projections. In this study, the possibility of using deep learning for the removal of noise in raw projections is investigated. The impact of loss functions on the detail preservation is analized in particular. For that purpose, training data is augmented following the physics driven approach of Eckert et al.1 In this method, an x-ray dose reduction is simulated. First pixel intensities are converted to the number of photons at the detector. Secondly, Poisson noise is enhanced in the x-ray image by simulating a decrease in the mean photon arrival rate. The Anscombe Transformation2 is then applied to construct signal independent white Gaussian noise. The augmented data is then used to train a neural network to estimate the noise. For training several loss functions are considered including the mean square error (MSE), the structural similarity index (SSIM)3 and the perceptual loss.4 Furthermore the ReLU-Loss1 is investigated, which is especially designed for mammogram denoising and prevents the network from noise overestimation. The denoising performance is then compared with respect to the preservation of small microcalcifications. Based on our current measurements, we demonstrate that the ReLU-Loss in combination with SSIM improves the denoising results.
Purpose: We compare the effects of scatter on the accuracy of areal bone mineral density (BMD) measurements obtained using two flat-panel detector (FPD) dual-energy (DE) imaging configurations: a dual-kV acquisition and a dual-layer detector. Methods: Simulations of DE projection imaging were performed with realistic models of x-ray spectra, scatter, and detector response for dual-kV and dual-layer configurations. A digital body phantom with 4 cm Ca inserts in place of vertebrae (concentrations 50 - 400 mg/mL) was used. The dual-kV configuration involved an 80 kV low-energy (LE) and a 120 kV high-energy (HE) beam and a single-layer, 43x43 cm FPD with a 650 μm cesium iodide (CsI) scintillator. The dual-layer configuration involved a 120 kV beam and an FPD consisting of a 200 μm CsI layer (LE data), followed by a 1 mm Cu filter, and a 550 μm CsI layer (HE data). We investigated the effects of an anti-scatter grid (13:1 ratio) and scatter correction. For the correction, the sensitivity to scatter estimation error (varied ±10% of true scatter distribution) was evaluated. Areal BMD was estimated from projection-domain DE decomposition. Results: In the gridless dual-kV setup, the scatter-to-primary ratio (SPR) was similar for the LE and HE projections, whereas in the gridless dual layer setup, the SPR was ~26% higher in the LE channel (top CsI layer) than in the HE channel (bottom layer). Because of the resulting bias in LE measurements, the conventional projection-domain DE decomposition could not be directly applied to dual-layer data; this challenge persisted even in the presence of a grid. In contrast, DE decomposition of dual-kV data was possible both without and with the grid; the BMD error of the 400 mg/mL insert was -0.4 g/cm2 without the grid and +0.3 g/cm2 with the grid. The dual-layer FPD configuration required accurate scatter correction for DE decomposition: a -5% scatter estimation error resulted in -0.1 g/cm2 BMD error for the 50 mg/mL insert and a -0.5 g/cm2 BMD error for the 400 mg/mL with a grid, compared to <0.1 g/cm2 for all inserts in a dual-kV setup with the same scatter estimation error. Conclusion: This comparative study of quantitative performance of dual-layer and dual-kV FPD-based DE imaging indicates the need for accurate scatter correction in the dual-layer setup due to increased susceptibility to scatter errors in the LE channel.
We investigate an image-based strategy to compensate for cardiac motion-induced artifacts in Digital Chest Tomosynthesis (DCT). We apply the compensation to conventional unidirectional vertical “↕” scan DCT and to a multidirectional circular trajectory "O" providing improved depth resolution. Propagation of heart motion into the lungs was simulated as a dynamic deformation. The studies investigated a range of motion propagation distances and scan times. Projection-domain retrospective gating was used to detect heart phases. Sparsely sampled reconstructions of each phase were deformably aligned to yield a motion compensated image with reduced sampling artifacts. The proposed motion compensation mitigates artifacts and blurring in DCT images both for “↕” and "O" scan trajectories. Overall, the “O” orbit achieved the same or better nodule structural similarity index in than the conventional “↕” orbit. Increasing the scan time improved the sampling of individual phase reconstructions.
Purpose: We investigate the feasibility of slot-scan dual-energy x-ray absorptiometry (DXA) on robotic x-ray platforms capable of synchronized source and detector translation. This novel approach will enhance the capabilities of such platforms to include quantitative assessment of bone quality using areal bone mineral density (aBMD), normally obtained only with a dedicated DXA scanner. Methods: We performed simulation studies of a robotized x-ray platform that enables fast linear translation of the x-ray source and flat-panel detector (FPD) to execute slot-scan dual-energy (DE) imaging of the entire spine. Two consecutive translations are performed to acquire the low-energy (LE, 80 kVp) and high-energy (HE, 120 kVp) data in <15 sec total time. The slot views are corrected with convolution-based scatter estimation and backprojected to yield tiled long-length LE and HE radiographs. Projection-based DE decomposition is applied to the tiled radiographs to yield (i) aBMD measurements in bone, and (ii) adipose content measurement in bone-free regions. The feasibility of achieving accurate aBMD estimates was assessed using a high-fidelity simulation framework with a digital body phantom and a realistic bone model covering a clinically relevant range of mineral densities. Experiments examined the effects of slot size (1 – 20 cm), scatter correction, and patient size/adipose content (waist circumference: 77 – 95 cm) on the accuracy and reproducibility of aBMD. Results: The proposed combination of backprojection-based tiling of the slot views and DE decomposition yielded bone density maps of the spine that were free of any apparent distortions. The x-ray scatter increased with slot width, leading to aBMD errors ranging from 0.2 g/cm2 for a 5 cm slot to 0.7 g/cm2 for a 20 cm slot when no scatter correction was applied. The convolution-based correction reduced the aBMD error to within 0.02 g/cm2 for slot widths <10 cm. Reproducible aBMD measurements across a range of body sizes (aBMD variability <0.1 g/cm2) were achieved by applying a calibration based on DE adipose thickness estimates from peripheral body sites. Conclusion: The feasibility of accurate and reproducible aBMD measurements on an FPD-based x-ray platform was demonstrated using DE slot scan trajectories, backprojection-domain decomposition, scatter correction, and adipose precorrection.
Purpose: We investigate cone-beam CT (CBCT) imaging protocols and scan orbits for 3D cervical spine imaging on a twin-robotic x-ray imaging system (Multitom Rax). Tilted circular scan orbits are studied to assess potential benefits in visualization of lower cervical vertebrae, in particular in low-dose imaging scenarios. Methods: The Multitom Rax system enables flexible scan orbit design by using two robotic arms to independently move the x-ray source and detector. We investigated horizontal and tilted circular scan orbits (up to 45° tilt) for 3D imaging of the cervical spine. The studies were performed using an advanced CBCT simulation framework involving GPU accelerated x-ray scatter estimation and accurate modeling of x-ray source, detector and noise. For each orbit, the x-ray scatter and scatter-to-primary ratio (SPR) were evaluated; cervical spine image quality was characterized by analyzing the contrast-to-noise ratio (CNR) for each vertebrae. Performance evaluation was performed for a range of scan exposures (263 mAs/scan – 2.63 mAs/scan) and standard and dedicated low dose reconstruction protocols. Results: The tilted orbit reduces scatter and increases primary detector signal for lower cervical vertebrae because it avoids ray paths crossing through both shoulders. Orbit tilt angle of 35° was found to achieve a balanced performance in visualization of upper and lower cervical spine. Compared with a flat orbit, using the optimized 35° tilted orbit reduces lateral projection SPR at the C7 vertebra by <40%, and increases CNR by 220% for C6 and 76% for C7. Adequate visualization of the vertebrae with CNR <1 was achieved for scan exposures as low as 13.2 mAs / scan, corresponding to ~3 mGy absorbed spine dose. Conclusion: Optimized tilted scan orbits are advantageous for CBCT imaging of the cervical spine. The simulation studies presented here indicate that CBCT image quality sufficient for evaluation of spine alignment and intervertebral joint spaces might be achievable at spine doses below 5 mGy.
The acquisition time of cone-beam CT (CBCT) systems is limited by different technical constraints. One important factor is the mechanical stability of the system components, especially when using C-arm or robotic systems. This leads to the fact that today’s acquisition protocols are performed at a system speed, where geometrical reproducibility can be guaranteed. However, from an application point of view faster acquisition times are useful since the time for breath-holding or being restraint in a static position has direct impact on patient comfort and image quality. Moreover, for certain applications, like imaging of extremities, a higher resolution might offer additional diagnostic value. In this work, we show that it is possible to intentionally exceed the conventional acquisition limits by accepting geometrical inaccuracies. To compensate deviations from the assumed scanning trajectory, a marker-free auto-focus method based on the gray-level histogram entropy was developed and evaluated. First experiments on a modified twin-robotic X-ray system (Multitom Rax, Siemens Healthcare GmbH, Erlangen, Germany) show that the acquisition time could be reduced from 14 s down to 9 s, while maintaining the same high-level image quality. In addition to that, by using optimized acquisition protocols, ultra-high-resolution imaging techniques become accessible.
Purpose: We optimize scan orbits and acquisition protocols for 3D imaging of the weight-bearing spine on a twin-robotic x-ray system (Multitom Rax). An advanced Cone-Beam CT (CBCT) simulation framework is used for systematic optimization and evaluation of protocols in terms of scatter, noise, imaging dose, and task-based performance in 3D image reconstructions. Methods: The x-ray system uses two robotic arms to move an x-ray source and a 43×43 cm2 flat-panel detector around an upright patient. We investigate two classes of candidate scan orbits, both with the same source-axis distance of 690 mm: circular scans with variable axis-detector distance (ADD, air gap) ranging from 400 to 800 mm, and elliptical scans, where the ADD smoothly changes between the anterior-posterior view (ADDAP) and the lateral view (ADDLAT). The study involved elliptical orbits with fixed ADDAP of 400 mm and variable ADDLAT, ranging 400 to 800 mm. Scans of human lumbar spine were simulated using a framework that included accelerated Monte Carlo scatter estimation and realistic models of the x-ray source and detector. In the current work, x-ray fluence was held constant across all imaging configurations, corresponding to 0.5 mAs/frame. Performance of circular and elliptical orbits was compared in terms of scatter and scatter-to-primary ratio (SPR) in projections, and contrast, noise, contrast-to-noise ratio (CNR), and truncation (field of view, FOV) in 3D image reconstructions. Results: The highest mean SPR was found in lateral views, ranging from ~5 at ADD of 300 mm to ~1.2 at ADD of 800 mm. Elliptical scans enabled image acquisition with reduced lateral SPR and almost constant SPR across projection angles. The improvement in contrast across the investigated range of air gaps (due to reduction in scatter) was ~2.3x for circular orbits and ~1.9x for elliptical orbits. The increase in noise associated with increased ADD was more pronounced for circular scans (~2x) compared to elliptical scans (~1.5x). The circular orbit with the best CNR performance (ADD=600 mm) yielded ~10% better CNR than the best elliptical orbit (ADDLAT=600 mm); however, the elliptical orbit increased FOV by ~16%. Conclusion: The flexible imaging geometry of the robotic x-ray system enables development of highly optimized scan orbits. Imaging of the weight-bearing spine could benefit from elliptical detector trajectories to achieve improved tradeoffs in scatter reduction, noise, and truncation.
Measurements of skeletal geometries are a crucial tool for the assessment of pathologies in orthopedics. Usually, those measurements are performed in conventional 2-D X-ray images. Due to the cone-beam geometry of most commercially available X-ray systems, effects like magnification and distortion are inevitable and may impede the precision of the orthopedic measurements. In particular measurements of angles, axes, and lengths in spine or limb acquisitions would benefit from a true 1-to-1 mapping without any distortion or magnification.
In this work, we developed a model to quantify these effects for realistic patient sizes and clinically relevant acquisition procedures. Moreover, we compared the current state-of-the-art technique for the imaging of length- extended radiographs, e. g. for spine or leg acquisitions (i. e. the source-tilt technique) with a slot-scanning method. To validate our model we conducted several experiments with physical as well as anthropomorphic phantoms, which turned out to be in good agreement with our model. To this end, we found, that the images acquired with the reconstruction-based slot-scanning technique comprise no magnification or distortion. This would allow precise measurements directly on images without considering calibration objects, which might be beneficial for the quality and workflow efficiency of orthopedic applications.
KEYWORDS: Digital breast tomosynthesis, Signal attenuation, Signal detection, Breast, Tissues, Image quality, Clinical trials, Digital mammography, 3D image reconstruction, Psychophysics
Detection of lesions is an essential part of making a diagnosis in mammography and therefore is a main focus in the development of algorithms built for image quality assessment. We propose a hybrid approach with an accurate lesion projection model and embedding of lesions into clinical images that already contain relevant structures of anatomical noise. Using an algebraic lesion model, lesions with different sizes and contrasts are generated. The projection algorithm incorporates the modeling of blur effects due to system movement and the physical extent of the anode. Signal and background patches are extracted and used to evaluate channelized Hotelling observers with Laguerre-Gauss channels and with Gabor channels. A four-alternative forced-choice study with five medical imaging experts is performed and the inter-reader agreement with and without the model observers is determined by using Fleiss' kappa. Analyzing three different sizes for tiny, dense lesions and four density levels for larger mass-like lesions we find a good detection rate of the tiny lesions for both human as well as model observers. The inter-reader agreement using the common interpretation of Fleiss' kappa is substantial or better. Comparing full-field digital mammography and digital breast tomosynthesis w.r.t. the different mass densities we find that human readers and model observers perform well on the DBT data and the detection rate drops with lesion contrast as expected. The inter-reader agreement here is fair for the lowest contrast and substantial for the denser cases. Both human readers and model observers show difficulty in detecting the low contrast lesions in FFDM images. The inter-reader agreement is rather poor among all readers. Overall, the results indicate a good agreement between human observers and model observers and a distinctive benefit of 3-D reconstruction over FFDMs for low contrast lesions.
X-ray cone-beam (CB) imaging is moving towards playing a large role in diagnostic radiology. Recently, an innovative, versatile X-ray system (Multitom Rax, Siemens Healthcare, GmbH, Forchheim, Germany) was introduced for diagnostic radiology. This system enables taking X-ray radiographs with high flexibility in patient positioning, as well as acquiring semi-circular short CB scans in a variety of orientations. We show here that this system can be further programmed to accurately scan the entire spine in the weight-bearing position. Such a diagnostic imaging capability has never been demonstrated so far. However, we may expect it to play an important clinical role as clinicians agree that spine diseases would be more accurately interpretable in the weight-bearing position. We implemented a geometry that provides complete data so that CB artifacts may be avoided. This geometry consists of two circular arcs connected by a line segment. We assessed immediate and short-term motion reproducibility, as well as ability to image the entire spine within a Rando phantom. Strongly encouraging results were obtained. Reproducibility with sub-mm accuracy was observed and the entire spine was accurately reconstructed.
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