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

{"title":"3D Diffusion Posterior Sampling for CT Reconstruction.","authors":"Peiqing Teng, Xiao Jiang, Liang Cai, Efren Lee, Ruoqiao Zhang, Jian Zhou, J Webster Stayman","doi":"10.1117/12.3047466","DOIUrl":"10.1117/12.3047466","url":null,"abstract":"<p><p>Diffusion models have demonstrated a powerful capability to generate a diversity of high quality images based on a training distribution. Recently, such diffusion models have been used in CT restoration and reconstruction via conditional generation. Diffusion posterior sampling (DPS) is a conditional generation method with several advantages, including unsupervised learning of the prior distribution and plug-and-play capabilities with different forward models to encompass different acquisition methods, protocols, etc. However, most current DPS work has focused on two-dimensional models for both the prior and system models. Almost all clinical CT systems are inherently three-dimensional using helical or cone-beam acquisitions. While the extension to 3D is mathematically straightforward, computational demands prohibit direct application on most platforms. In this research, we propose strategies for 3D DPS CT reconstruction using a 3D neural network to learn the prior distribution. We develop modifications to a standard DPS algorithm to substantially reduce memory requirements and to accelerate the sampling speed. We evaluate different alternatives that permit 3D DPS in realistic CT volume sizes and compare relative merits of each strategy.</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/PMC12330245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818582","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":"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":"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":"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":"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":"Exploring connections of spectral analysis and transfer learning in medical imaging.","authors":"Yucheng Lu, Dovile Juodelyte, Jonathan D Victor, Veronika Cheplygina","doi":"10.1117/12.3047670","DOIUrl":"10.1117/12.3047670","url":null,"abstract":"<p><p>In this paper, we use spectral analysis to investigate transfer learning and study model sensitivity to frequency shortcuts in medical imaging. By analyzing the power spectrum density of both pre-trained and fine-tuned model gradients, as well as artificially generated frequency shortcuts, we observe notable differences in learning priorities between models pre-trained on natural vs medical images, which generally persist during fine-tuning. We find that when a model's learning priority aligns with the power spectrum density of an artifact, it results in overfitting to that artifact. Based on these observations, we show that source data editing can alter the model's resistance to shortcut learning. Code available at: https://github.com/YCL92/Shortcut-PSD.</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/PMC12201968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509909","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":"Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment.","authors":"Jorge Tapias Gomez, Aneesh Rangnekar, Hannah Williams, Hannah M Thompson, Julio Garcia-Aguilar, Joshua Jesse Smith, Harini Veeraraghavan","doi":"10.1117/12.3046794","DOIUrl":"10.1117/12.3046794","url":null,"abstract":"<p><p>Endoscopic images are used at various stages of rectal cancer treatment starting from cancer screening and diagnosis, during treatment to assess response and toxicity from treatments such as colitis, and at follow-up to detect new tumor or local regrowth. However, subjective assessment is highly variable and can underestimate the degree of response in some patients, subjecting them to unnecessary surgery, or overestimating response that places patients at risk of disease spread. Advances in deep learning have shown the ability to produce consistent and objective response assessments for endoscopic images. However, methods for detecting cancers, regrowth, and monitoring response during the entire course of patient treatment and follow-up are lacking. This is because automated diagnosis and rectal cancer response assessment require methods that are robust to inherent imaging illumination variations and confounding conditions (blood, scope, blurring) present in endoscopy images as well as changes to the normal lumen and tumor during treatment. Hence, a hierarchical shifted window (Swin) transformer was trained to distinguish rectal cancer from normal lumen using endoscopy images. Swin, as well as two convolutional (ResNet-50, WideResNet-50), and the vision transformer architectures, were trained and evaluated on follow-up longitudinal images to detect LR on in-distribution (ID) private datasets as well as on out-of-distribution (OOD) public colonoscopy datasets to detect pre/non-cancerous polyps. Color shifts were applied using optimal transport to simulate distribution shifts. Swin and ResNet models were similarly accurate in the ID dataset. Swin was more accurate than other methods (follow-up: 0.84, OOD: 0.83), even when subject to color shifts (follow-up: 0.83, OOD: 0.87), indicating the capability to provide robust performance for longitudinal cancer assessment.</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/PMC12463470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187594","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":"High-dose-rate Brachytherapy Planning with Dendrite Cross-Attention UNet.","authors":"Sourav Saini, Yawen Wei, Jingzhao Rong, Xiaofeng Liu","doi":"10.1117/12.3046746","DOIUrl":"10.1117/12.3046746","url":null,"abstract":"<p><p>Treatment of cervical cancer commonly involves high-dose-rate brachytherapy (HDR-BT), a procedure that requires precise and efficient planning to achieve the best patient outcomes. Historically, the HDR-BT planning process has been labor-intensive and largely dependent on the expertise of the clinician, resulting in potential inconsistencies in the quality of treatment. To overcome this issue, we propose an innovative method that employs advanced deep-learning models to improve HDR-BT planning. This paper presents the <b>Dendrite Cross-Attention UNet (DCA-UNet)</b>, which features a sophisticated dendritic structure comprising a primary branch for stacked inputs and three auxiliary branches dedicated to the segmentation of the clinical target volume (CTV), bladder, and rectum. This architecture enhances the model's understanding of organ-at-risk (OAR) areas, thereby improving dose prediction accuracy. Extensive evaluations reveal that DCA-UNet significantly enhances the precision of HDR-BT dose predictions across different applicator types. Our findings indicate that DCA-UNet consistently outperforms both traditional UNet and the more recent SwimUNetr models. By advancing the use of cross-attention mechanisms in deep learning frameworks, this research aids in the standardization of HDR-BT planning and opens up promising possibilities for future advancements in cervical cancer care.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13408 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884422","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}