SignificanceStandardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed.AimWe aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems.ApproachWe quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system.ResultsWe show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼35 dB (SNR), ∼8.65 a.u. (contrast), and ∼0.67 a.u. (BM score).ConclusionsThe definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.
Oral cancer has a poor five-year survival rate and has not improved much in the past two decades which is due to late diagnosis. In current clinical practice analysis of Haematoxylin Eosin stained tissue biopsy is considered as a golden standard which is rather painful and routine check is not possible. In this regard, native fluorescence spectroscopy has been considered to discriminate cancer tissue based on relative alterations in the level of tryptophan. To estimate relative variations of tryptophan at different layers of tissue fluorescence polarization gating technique has been adopted which is based on the principle that the light from the superficial layer of tissue partially retain the polarization plane of incident light as they are less scattered while light from the deeper layer is completely depolarized due to multiple scattering. Integrated intensity of tryptophan was quantified, and subsequent statistical analysis has been carried out to evaluate the diagnostic potentiality of the proposed technique. It was found that the fractional variation of tryptophan in the superficial layer to the deeper layer was found to be statistically more significant in discriminating oral cancer than cumulative tryptophan in both layers.
We present here an open-source platform capable of simulating Optical Coherence Tomography (OCT) images to be used by the community. Such a simulator can have a variety of applications such as the training of neural networks. Our first-generation Monte Carlo simulator quantifies detected photons path length. The code achieves reasonable runtime by exploiting a bias scattering scheme. The results are then interpreted to produce an OCT A-line. A Github platform was implemented with necessary documentation (e.g. readme file and instructions guide) to allow a variety of application and geometry as well as community-based improvements over time.
Near-infrared fluorescence (NIRF) angiography has been applied for intraoperative visualization of neurovascular circulation and pathologies such as aneurysms, as well as to enhance contrast in clinical imaging of retinal fundus microvasculature. The ability to quantitatively evaluate and compare the performance of NIRF imaging devices including contrast, resolution and linearity under biologically realistic, yet reproducible conditions would facilitate innovation and clinical translation of this technology. Towards development of methods that can fill this role, we have generated 3D-printed, image-defined tissue simulating phantoms at macro and micro scales. The macro-scale phantom was developed based on an MRI image volume of a human head. This map was segmented into white matter, gray matter and vessel regions and edited to provide a suitable file for 3D printing. The phantom was then printed with an Objet260 Connex3 printer using a material with a biologically relevant NIR scattering coefficient. The micro-scale phantom is based on a fundus camera image of a human retina. This phantom was printed using a Nanoscribe Photonic Professional GT printer with sub-micron resolution, but a maximum print volume of approximately 1 mm3. To demonstrate the neurovascular phantoms for NIRF imaging system, channels were injected with a solution of hemoglobin and Indocyanine Green and then imaged with CCD-based macro- and micro- NIRF imaging system. Overall, these approaches for fabricating biomimetic phantoms hold significant promise for evaluation of NIRF angiography devices image quality in a standardized, yet realistic manner.
KEYWORDS: Cancer, Endoscopy, Imaging systems, Breast cancer, Luminescence, Tissues, Fluorescence spectroscopy, Spectroscopy, In vivo imaging, In vitro testing
Despite various technological advancements in cancer diagnosis, the mortality rates were not decreased significantly. We aim to develop a novel optical imaging tool to assist cancer diagnosis effectively. Fluorescence spectroscopy/imaging is a fast, rapid, and minimally invasive technique which has been successfully applied to diagnosing cancerous cells/tissues. Recently, the ratiometric imaging of intrinsic fluorescence of reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), as pioneered by Britton Chance and the co-workers in 1950-70’s, has gained much attention to quantify the physiological parameters of living cells/tissues. The redox ratio, i.e., FAD/(FAD+NADH) or FAD/NADH, has been shown to be sensitive to various metabolic changes in in vivo and in vitro cells/tissues. Optical redox imaging has also been investigated for providing potential imaging biomarkers for cancer transformation, aggressiveness, and treatment response. Towards this goal, we have designed and developed a novel fiberoptic-based needle redox imager (NRI) that can fit into an 11G clinical coaxial biopsy needle for real time imaging during clinical cancer surgery. In the present study, the device is calibrated with tissue mimicking phantoms of FAD and NADH along with various technical parameters such as sensitivity, dynamic range, linearity, and spatial resolution of the system. We also conducted preliminary imaging of tissues ex vivo for validation. We plan to test the NRI on clinical breast cancer patients. Once validated this device may provide an effective tool for clinical cancer diagnosis.
Near-infrared fluorescence (NIRF) imaging has gained much attention as a clinical method for enhancing visualization of cancers, perfusion and biological structures in surgical applications where a fluorescent dye is monitored by an imaging system. In order to address the emerging need for standardization of this innovative technology, it is necessary to develop and validate test methods suitable for objective, quantitative assessment of device performance. Towards this goal, we develop target-based test methods and investigate best practices for key NIRF imaging system performance characteristics including spatial resolution, depth of field and sensitivity. Characterization of fluorescence properties was performed by generating excitation-emission matrix properties of indocyanine green and quantum dots in biological solutions and matrix materials. A turbid, fluorophore-doped target was used, along with a resolution target for assessing image sharpness. Multi-well plates filled with either liquid or solid targets were generated to explore best practices for evaluating detection sensitivity. Overall, our results demonstrate the utility of objective, quantitative, target-based testing approaches as well as the need to consider a wide range of factors in establishing standardized approaches for NIRF imaging system performance.
Fluorescence of Protein has been widely used in diagnostic oncology for characterizing cellular metabolism. However, the intensity of fluorescence emission is affected due to the absorbers and scatterers in tissue, which may lead to error in estimating exact protein content in tissue. Extraction of intrinsic fluorescence from measured fluorescence has been achieved by different methods. Among them, Monte Carlo based method yields the highest accuracy for extracting intrinsic fluorescence. In this work, we have attempted to generate a lookup table for Monte Carlo simulation of fluorescence emission by protein. Furthermore, we fitted the generated lookup table using an empirical relation. The empirical relation between measured and intrinsic fluorescence is validated using tissue phantom experiments. The proposed relation can be used for estimating intrinsic fluorescence of protein for real-time diagnostic applications and thereby improving the clinical interpretation of fluorescence spectroscopic data.
Cancer is one of the most common threat to human beings and it increases at an alarming level around the globe. In recent years, due to the advancements in opto-electronic technology, various optical spectroscopy techniques have emerged to assess the photophysicochemical and morphological conditions of normal and malignant tissues in micro as well as in macroscopic scale. In this regard, diffuse reflectance spectroscopy is considered to be the simplest, cost effective and rapid technique in diagnosis of cancerous tissues. In the present study, the hemoglobin concentration in normal and cancerous oral tissues was quantified and subsequent statistical analysis has been carried out to verify the diagnostic potentiality of the technique.
Cancer is one of the most common human threats around the world and diagnosis based on optical spectroscopy especially fluorescence technique has been established as the standard approach among scientist to explore the biochemical and morphological changes in tissues. In this regard, the present work aims to extract spectral signatures of the various fluorophores present in oral tissues using parallel factor analysis (PARAFAC). Subsequently, the statistical analysis also to be performed to show its diagnostic potential in distinguishing malignant, premalignant from normal oral tissues. Hence, the present study may lead to the possible and/or alternative tool for oral cancer diagnosis.
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