Proceedings of SPIE--the International Society for Optical Engineering最新文献

{"title":"Design of a \"3.5th generation\" photon counting detector CT architecture for higher spatial resolution and decreased ring artifact.","authors":"Scott S Hsieh","doi":"10.1117/12.3045834","DOIUrl":"10.1117/12.3045834","url":null,"abstract":"<p><p>Fourth generation CT was originally conceived to reduce ring artifacts from inhomogeneities in early energy integrating detector (EID) modules. These inhomogeneities are well controlled in modern EID modules but have reappeared in photon counting detector (PCD) modules, where fabrication techniques are not yet mature. Fourth generation CT was abandoned decades ago because of its high cost and scatter. We propose grafting its central insight into 3rd generation CT using a compact, modified X-ray source that operates with a high-speed flying focal spot over a limited range of travel (e.g., 1 cm). The PCD must be modified so that measured data is rebinned on-the-fly, so that data bandwidth requirements across the slip ring are unchanged. In this geometry, data from each PCD pixel is distributed to a several contiguous radial indices. This reduces ring artifacts that stem from pixel inhomogeneities and also allows recovery of missing data that is due to dead pixels or occlusion by the anti-scatter grid. Finally, if the dwell time at each focal spot location is very short (sub-microsecond), the maximum instantaneous surface temperature at the anode is reduced. This could be used to reduce focal spot size while maintaining the thermal limit of the focal track.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Harmonization: A Systematic Analysis of Various Task-based Scenarios.","authors":"Shao-Jun Xia, Liesbeth Vancoillie, Saman Sotoudeh-Paima, Mojtaba Zarei, Fong Chi Ho, Fakrul Islam Tushar, Xiaoyang Chen, Lavsen Dahal, Kyle J Lafata, Ehsan Abadi, Joseph Y Lo, Ehsan Samei","doi":"10.1117/12.3047096","DOIUrl":"https://doi.org/10.1117/12.3047096","url":null,"abstract":"<p><p>In medical imaging, harmonization plays a crucial role in reducing variability arising from diverse imaging devices and protocols. Patient images obtained under different computed tomography (CT) scan conditions may show varying performance when analyzed using an artificial intelligence model or quantitative assessment. This necessitates the need for harmonization. Virtual imaging trial (VIT) through digital simulation can be used to develop and assess the effectiveness of harmonization models to minimize data variability. The purpose of this study was to assess the utility of a VIT platform for harmonization across a range of lung imaging scenarios. To ensure consistent and reliable analysis across different virtual imaging datasets, we conducted a multi-objective assessment encompassing three typical task-based scenarios: lung structure segmentation, chronic obstructive pulmonary disease (COPD) quantification, and lung nodule quantification. A physics-informed deep neural network was applied as the unified harmonization model for all three tasks. Evaluation results before and after harmonization reveal three findings: 1) modestly improved Dice scores and reduced Hausdorff Distances at 95th Percentile in lung structure segmentation; 2) decreased variation in biomarkers and radiomics features in COPD quantification; and 3) increased number of radiomics features with high intraclass correlation coefficient in lung nodule quantification. The results demonstrate the significant potential of harmonization across various task-based scenarios and provide a benchmark for the design of efficient harmonizers.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Accuracy of CT Protocols for Cross-sectional and Longitudinal Assessment of COPD: A Virtual Imaging Study.","authors":"Mridul Bhattarai, Daniel W Shin, Fong Chi Ho, Saman Sotoudeh-Paima, Ilmar Hein, Steven Ross, Naruomi Akino, Kirsten L Boedeker, Ehsan Samei, Ehsan Abadi","doi":"10.1117/12.3046945","DOIUrl":"https://doi.org/10.1117/12.3046945","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD), encompassing chronic bronchitis and emphysema, requires precise quantification through CT imaging to accurately assess disease severity and progression. However, inconsistencies in imaging protocols often lead to unreliable measurements. This study aims to optimize CT acquisition and reconstruction protocols for cross-sectional and longitudinal CT measurements of COPD using a virtual (<i>in-silico</i>) imaging framework. We developed human models at various stages of emphysema and bronchitis, informed by the COPDGene cohort. The specifications of a clinical CT scanner (Aquilion ONE Prism, Canon Medical Systems) were integrated into a CT simulator. This simulation framework was validated against experimental data. The analysis focused on the impact of tube current and kernel sharpness on two COPD biomarkers: LAA-950 (percentage of lung voxels with attenuation less than -950 HU) and Pi10 (the square root of the wall area around an airway with an internal perimeter of 10 mm) and mean absolute error (MAE; a voxel-wise error metric for emphysema density measurements). The increase in dose level showed minimal impact on the Pi10 measurements, but affected the LAA-950, with a reduction in variability observed at higher dose levels. Increasing kernel sharpness introduced variability in the LAA-950 and Pi10 measurements and higher MAE with sharper kernels. Longitudinal analysis demonstrated that kernel sharpness contributed more to variability in the COPD biomarker measurements over time compared to dose level. Similarly, cross-sectional assessments showed that an increase in MAE, while a decrease in Pi10 measurement error with sharper kernels. The study underlines the need for standardized task-specific imaging protocols to enhance the reliability and accuracy of COPD assessments, thus improving diagnostic precision and patient assessments.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring bias in spectral CT material decomposition: a simulation-based approach.","authors":"Milan Smulders, Dufan Wu, Rajiv Gupta","doi":"10.1117/12.3047261","DOIUrl":"https://doi.org/10.1117/12.3047261","url":null,"abstract":"<p><strong>Introduction -: </strong>Computed tomography (CT) imaging has seen significant advancements with the introduction of spectral CT, which improves material differentiation by acquiring images at multiple energy levels. Photon-counting CT (PCCT) is an emerging technique to implement spectral CT with photon counting detectors that may discriminate detected photon energies to different energy bins. Material differentiation is achieved by decomposing the acquired data into two-material models such as brain/bone or brain/iodine. However, such decomposition is susceptible to bias due to inaccurate physical modeling. In this study, we aim to study the relationship between the material decomposition bias and the energy thresholds used in PCCT, under ideal, noiseless models.</p><p><strong>Methods -: </strong>A projection-based material decomposition model was used to directly decompose projection data. Bias simulation was performed using a Shepp-Logan phantom with brain/bone and brain/iodine as basis materials. X-ray spectra were generated using a fixed 10 keV threshold and a varying threshold sampled from 20 to 90 keV, with extra sampling points around iodine's k-edge. Virtual monoenergetic images (VMIs) at 60 keV and 140 keV were analyzed to evaluate bias for each material and material pair.</p><p><strong>Results -: </strong>Lower energy thresholds (<40 keV) introduced a larger bias in material decomposition, with peaks observed between 30 and 40 keV, particularly around the k-edge of iodine. The bias generally decreased with increasing thresholds above 50 keV, especially for non-basis materials. This trend was consistent across brain/bone and brain/iodine bases and for both 60 and 140 keV VMIs.</p><p><strong>Conclusion -: </strong>Energy thresholds significantly affect the accuracy of projection-based material decomposition in PCCT. Greater differences between thresholds lead to reduced decomposition bias. Future research should incorporate non-ideal detector responses and noise, as well as explore image-domain decomposition and real phantom studies with possible translation to improve patient care.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12060251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing parylene and photoconductor thickness in indirect conversion amorphous selenium detectors.","authors":"Kaitlin Hellier, Hamid Mirzanezhad, Molly McGrath, Paul Pryor, Ivan Mollov, Shiva Abbaszadeh","doi":"10.1117/12.3047617","DOIUrl":"https://doi.org/10.1117/12.3047617","url":null,"abstract":"<p><p>Amorphous selenium (a-Se) provides an opportunity for a low cost, large area, avalanche photodetector for use in indirect conversion detectors. However, its bandgap of 2.2 eV reduces the response at long wavelengths, specifically the 550 nm green light emitted by CsI:Tl scintillators, limits its application. Incorporating tellurium into the a-Se conversion layer is known to reduce the bandgap and increase sensitivity at these longer wavelengths. Previous studies have demonstrated this effectiveness and have shown that high conversion efficiencies can be achieved despite the reduced carrier mobility and lifetime of Se-Te. This group has proposed utilizing a Se-Te layer in an indirect conversion flat panel detector with 85 um pixel pitch, implementing a parylene hole blocking layer. Results of that work demonstrated the need for optimization of the thickness of those layers to achieve high sensitivity, reasonable leakage, and low lag and ghosting. In this study, we evaluate the effects of varying the parylene layer thickness and the photodetector conversion layer for single pixel Se-Te devices. We find that, while thicker Se-Te and parylene devices achieve low dark current, anticipated signal levels, and low lag, thinner samples suffer from signal loss and residual charge in the device. Varying the thickness of parylene leads to tradeoffs in dark current and residual charge, each of which is important in the performance of the final imager. To make use of parylene as a hole blocking layer, thicker photoconductor and parylene layers must be employed.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12021020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bundle-wise functional connectivity density and fractional amplitude of low-frequency fluctuations decrease in white matter in preclinical Alzheimer's disease and are associated with Aβ levels and cognition.","authors":"Yukie Chang, Lyuan Xu, Chenyu Gao, Nazirah Mohd Khairi, John C Gore, Bennett A Landman, Yurui Gao","doi":"10.1117/12.3046835","DOIUrl":"10.1117/12.3046835","url":null,"abstract":"<p><p>Neurophysiological changes associated with Alzheimer's disease (AD) begin decades before clinical symptoms emerge, during preclinical AD. Functional abnormalities in white matter (WM) at this preclinical stage remain largely unexplored. We obtained resting-state functional magnetic resonance imaging (rs-fMRI) data of 295 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and evaluated bundle-wise functional connectivity density (FCD) and fractional amplitude of low-frequency fluctuations (fALFF) across 46 bundles, which reflects the strength of synchronizations of BOLD dynamics between each WM bundle and whole-brain 200 GM parcels, and spontaneous neural activity of each WM bundle, respectively. To mitigate site/scanner effects on the metrics, ComBat harmonization was applied to the data. We then performed permutation tests (n=5,000) on each harmonized metric for each bundle to determine differences in FCD and fALFF in preclinical AD relative to controls, adjusting for sex, age, and education using multiple linear regression. Linear correlations of the metrics with the pathological biomarker beta-amyloid (Aβ) and cognitive scores (mPACC and ADAS11) were assessed using general linear models. Multiple comparisons were corrected via a false discovery rate (FDR). We found that preclinical AD patients had reduced FCD and fALFF in specific WM bundles, such as cingulate and hippocampal cingulum, compared to controls (FDR corrected <i>p</i> < 0.05), some of which were associated with poorer cognitive performance and greater Aβ accumulation (FDR corrected <i>p</i> < 0.05). This study, to the best of our knowledge, is the first to examine bundle-wise FCD and fALFF of WM in preclinical AD using a large-scale, multi-site, cross-sectional dataset, suggesting potential applications of these metrics for assessing preclinical AD with rs-fMRI.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13410 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12068856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic Segmentation of TB in Chest X-rays: a New Dataset and Generalization Evaluation.","authors":"Karthik Kantipudi, Vy Bui, Hang Yu, Y M Fleming Lure, Stefan Jaeger, Ziv Yaniv","doi":"10.1117/12.3047222","DOIUrl":"10.1117/12.3047222","url":null,"abstract":"<p><p>According to the 2023 World Health Organization report, an estimated 7.5 million people were diagnosed with tuberculosis (TB) in 2022. TB triaging is often performed using chest X-rays (CXRs), with significant efforts invested in automating this task using deep learning. A key concern with algorithms that output image-level labels, in our context TB/not-TB, is that they do not provide an explicit explanation with respect to how the output was obtained, limiting the ability of user oversight. Semantic segmentation of TB lesions can enable human supervision as part of the diagnosis process. This work presents a new dataset, TB-Portals SIFT, which enables semantic segmentation of TB lesions in CXRs (6,328 images with 10,435 pseudo-label lesion instances). Using this data, ten semantic segmentation models from the UNet and YOLOv8-seg architectures were evaluated in a five-fold cross validation study. The best performing segmentation models from each architecture, nnUNet(ResEnc XL) and YOLOv8m-seg and their ensemble were then evaluated for generalization on related classification and object detection tasks. Additionally, several binary DenseNet121 classifiers were trained, and their classification generalization performance was compared to that of the semantic segmentation-based classifier. Results show that the segmentation-based approach achieved better generalizability than the DenseNet121 classifiers and that the ensemble of the models from the two architectures was the most stable, closely matching or exceeding the performance of all other models across the tasks of segmentation, classification, and object detection. The dataset is publicly available from the NIAID TB Portals program after signing a data usage agreement which is available from https://tbportals.niaid.nih.gov/download-data.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13407 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projection Embedded Schrödinger Bridge for CT Sparse View Reconstruction.","authors":"Yuang Wang, Pengfei Jin, Siyeop Yoon, Matthew Tivnan, Quanzheng Li, Li Zhang, Zhiqiang Chen, Dufan Wu","doi":"10.1117/12.3048484","DOIUrl":"10.1117/12.3048484","url":null,"abstract":"<p><p>In this work, we proposed the Projection Embedded Schrödinger Bridge (PESB) for CT sparse view reconstruction. PESB constructs Schrödinger Bridges between the distribution of Filtered Back-Projection (FBP) reconstructed images and the distribution of clean images conditioned on measured projections. By embedding projections into the marginal conditions, data consistency is inherently incorporated into the generative process. Experimental results validate the effectiveness of PESB, demonstrating its superior performance in CT sparse view reconstruction compared to several diffusion-based models.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 4D atlas of diffusion-informed spatial smoothing windows for BOLD signal in white matter.","authors":"Adam M Saunders, Gaurav Rudravaram, Nancy R Newlin, Michael E Kim, John C Gore, Bennett A Landman, Yurui Gao","doi":"10.1117/12.3047240","DOIUrl":"https://doi.org/10.1117/12.3047240","url":null,"abstract":"<p><p>Typical methods for preprocessing functional magnetic resonance images (fMRI) involve applying isotropic Gaussian smoothing windows to denoise blood oxygenation level-dependent (BOLD) signals, a process which spatially smooths white matter signals that occur along anisotropically-oriented fibers. Abramian et al. have proposed diffusion-informed spatial smoothing (DSS) filters to smooth white matter in a physiologically-informed manner. However, these filters rely on paired diffusion MRI and fMRI data, which are not always available. Here, we create DSS windows for smoothing fMRI data in the white matter based on the Human Connectome Project Young Adult population-averaged atlas of fiber orientation distribution functions. We smooth fMRI data from 63 subjects using the atlas-based DSS windows and compare the results with fMRI data smoothed with isotropic Gaussian windows at 1.04 mm full-width half-max (FWHM) and 3 mm FWHM. Compared to isotropic Gaussian windows, the atlas-based DSS windows result in fMRI data with a significantly higher local functional connectivity measured with regional homogeneity (ReHo, <i>p</i> < 0.001). The DSS atlas results in biologically informed regions of interest identified through independent component analysis that more closely agree with regions from a diffusion MRI-based white matter atlas. The DSS atlas generated here allows for diffusion-informed smoothing of fMRI data when additional diffusion MRI data are not available. The DSS atlas and code are available online (https://github.com/MASILab/dss_fmri_atlas).</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13406 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12074659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Task-specific deep learning-based denoising for UHR cardiac PCD-CT adaptive to imaging conditions and patient characteristics: Impact on image quality and clinical diagnosis and quantitative assessment.","authors":"Madeleine Wilson, Shaojie Chang, Emily K Koons, Cynthia H McCollough, Shuai Leng","doi":"10.1117/12.3047283","DOIUrl":"https://doi.org/10.1117/12.3047283","url":null,"abstract":"<p><p>Ultra-high-resolution (UHR) photon-counting detector (PCD) CT offers superior spatial resolution compared to conventional CT, benefiting various clinical areas. However, the UHR resolution also significantly increases image noise, which can limit its clinical adoption in areas such as cardiac CT. In clinical practice, this image noise varies substantially across imaging conditions, such as different diagnostic tasks, patient characteristics (e.g., size), scan protocols, and image reconstruction settings. To address these challenges and provide the full potential of PCD-CT for optimal clinical performance, a convolutional neural network (CNN) denoising algorithm was developed, optimized, and tailored to each specific set of conditions. The algorithm's effectiveness in reducing noise and its impact on coronary artery stenosis quantification across different patient size categories (small: water equivalent diameter <300 mm, medium: 300-320 mm, and large: >320 mm) were objectively assessed. Reconstruction kernels at different sharpness, from Bv60 to Bv76, were investigated to determine optimal settings for each patient size regarding image quality and quantitative assessment of coronary stenosis (in terms of percent diameter stenosis). Our findings indicate that for patients with a water equivalent diameter less than 320 mm, CNN-denoised Bv72 images provide optimal image quality, less blooming artifact, and reduced percent diameter stenosis compared to routine images, while for patients with water equivalent diameter over 320 mm, CNN-denoised Bv60 images are preferable. Quantitatively, the CNN reduces noise-by 85% compared to the input images and 53% compared to commercial iterative reconstructions at strength 4 (QIR4)-while maintaining high spatial resolution and a natural noise texture. Moreover, it enhances stenosis quantification by reducing the percent diameter stenosis measurement by 52% relative to the input and 24% relative to QIR4. These improvements demonstrate the capability of CNN denoising in UHR PCD-CT to enhance image quality and quantitative assessment of coronary artery disease in a manner that is adaptive to patient characteristics and imaging conditions.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13405 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12076256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}