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

{"title":"A Novel Attention-based Network for Geometry Reconstruction with Error Estimation from Medical Images.","authors":"Linchen Qian, Jiasong Chen, Linhai Ma, Timur Urakov, Weiyong Gu, Liang Liang","doi":"10.1117/12.3038529","DOIUrl":"10.1117/12.3038529","url":null,"abstract":"<p><p>Instance segmentation of anatomical structures from medical images can help enhance clinical outcomes such as disease diagnosis, surgical planning accuracy, and treatment efficacy. However, since segmentation masks often lack point-to-point correspondence between patients, instance segmentation masks may not be directly applicable for clinical studies that require measurement of medical parameters defined on an anatomical shape atlas. For such applications, meshes with correspondence between patients are preferred representations of object geometries. The conversion from segmentation masks to meshes can be error-prone due to segmentation artifacts, and therefore, it is desirable to directly obtain mesh representations of object geometries from medical image data, bypassing segmentation masks. In this work, we propose novel attention-based neural networks for geometry reconstruction and error estimation, which offers a direct pathway from medical images to high-quality mesh representations. We introduce an innovative attention-based feature extraction network and incorporate image self-attention and shape self-attention with cross-attention between them to capture consistent features. Based on the extracted features, we develop a geometry reconstruction network that deforms a mesh template to reconstruct the geometry of the object, which automatically ensures mesh correspondence. In addition, we design a shape error estimation network to evaluate the reliability of the output from geometry reconstruction, that is, to estimate the point-to-point error of a reconstructed geometry. We demonstrate our approach for the application of lumbar spine geometry reconstruction and compare our geometry reconstruction network with UNet++, UTNet, Swin UnetTR, SLT-Net, and nnUNet, using the Dice metric. In this application, our geometry reconstruction network has much higher accuracy and artifact-free segmentation results, and our shape error estimation network facilitates quality control for clinical use. The source code is available at https://github.com/linchenq/SPIE2025-GoemReconstruction-with-ShapeErrorNet.</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/PMC12439167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082592","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}

{"title":"Vasculature-informed spatial smoothing of white matter functional magnetic resonance imaging.","authors":"Adam M Saunders, Michael E Kim, Kurt G Schilling, John C Gore, Bennett A Landman, Yurui Gao","doi":"10.1117/12.3047140","DOIUrl":"https://doi.org/10.1117/12.3047140","url":null,"abstract":"<p><p>Blood oxygenation level-dependent (BOLD) signals in white matter in the brain are anisotropically oriented, so that typical isotropic Gaussian spatial smoothing (GSS) of functional magnetic resonance images (fMRI) blurs across anatomical distributions. Abramian et al. developed a graph signal processing approach to smooth fMRI data along white matter fibers using diffusion MRI (diffusion-informed spatial smoothing, DSS). BOLD signals are modulated by the volume and oxygenation of blood carried by the vasculature, so we extend this method to provide vasculature-informed spatial smoothing (VSS). We collected susceptibility-weighted images and applied a Frangi filter to identify the peak vasculature direction in each voxel, alongside co-registered diffusion MRI and resting-state fMRI, weighting the VSS graph by the agreement of the vasculature directions aligned onto the graph's edges. We acquired resting-state fMRI at 7T using a repetition time of 1.5 seconds and 400 time points. Applying the DSS and VSS filters significantly increased the local functional connectivity measured using regional homogeneity (ReHo) compared to GSS (<i>p</i> < 0.01 using a paired <i>t</i>-test), but not when comparing DSS and VSS (<i>p</i> = 0.06). Independent component analysis resulted in less noisy components that agree better with labels from a white matter atlas with a significantly higher Dice score from the VSS filter compared to GSS (<i>p</i> < 0.05 using the Mann-Whitney U-test), and the VSS filter and DSS filter performed comparably (<i>p</i> = 0.06). In this pilot analysis, we find that fMRI data smoothed using VSS are comparable to results generated using DSS. The filtering code is available online (https://github.com/MASILab/vss_fmri).</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/PMC12074660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082654","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":"Attention Rings for Shape Analysis and Application to MRI Quality Control.","authors":"Florian Davaux, Lucas Valladon, Lucie Dole, Jean Christophe Fillion, Beatriz Paniagua, Martin Styner, Juan Carlos Prieto","doi":"10.1117/12.3047233","DOIUrl":"10.1117/12.3047233","url":null,"abstract":"<p><p>The Adolescent Brain Cognitive Development (ABCD) Study collects extensive neuroimaging data, including over 20,000 MRI sessions, to understand brain development in children. Ensuring high-quality MRI data is essential for accurate analysis, but manual Quality Control (QC) is impractical for large datasets due to time and resource constraints. We propose an automated QC method using an ensemble model that leverages metrics from FSQC and a novel deep learning model for brain shape analysis that uses cortical thickness, curvature, sulcal depth, and surface area as input features. The ensemble model achieved an accuracy of 76%, while our method achieved an accuracy of 72.62%, with balanced precision, recall, and F1 scores for both classes. This automated method promises to improve QC processes and accelerate the analysis of ABCD data.</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/PMC12096335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129840","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":"Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs).","authors":"Sai Spandana Chintapalli, Sindhuja T Govindarajan, Haochang Shou, Yong Fan, Hao Huang, Christos Davatzikos","doi":"10.1117/12.3040541","DOIUrl":"10.1117/12.3040541","url":null,"abstract":"<p><p>We present a preliminary analysis of a GAN-based normative modeling technique for capturing individual-level deviations in brain measures, addressing heterogeneity in neurological disorders. By leveraging self-supervised training on pseudo-synthetically simulated patient data, our method detects disease-related effects without the need for large, disease-specific datasets. We demonstrate the versatility of this approach by applying it to structural MRI and resting-state fMRI data, identifying neuroanatomical and functional connectivity deviations in Alzheimer's disease (AD) and Traumatic Brain Injury (TBI). This model's ability to accurately capture disease-related abnormalities in brain measures highlights its potential as a powerful tool for personalized diagnosis and the study of brain disorders, opening new avenues for research.</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/PMC12143270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251221","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":"Democratizing advanced surgical guidance: decoupling the state-of-the-art from tertiary centers and breaking trail for autonomous robotic surgery in austere environments.","authors":"Eric R Henderson, David H Gracias, Venkat M Ramakrishnan, Stas Tiomkin, Juan P Wachs, Daniel A Wollin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Surgery involves iterative identification of anatomical structures and intervention upon them. In recent years, machine-based tissue recognition has advanced substantially, enhancing the safety and efficacy of medical procedures by reducing uncertainty about structure identity through quantitative evaluation (e.g., tissue density, optical properties, fluorescence contrast). However, while tissue-identifying tools have progressed rapidly, the development of intervention tools has lagged. It is worth considering the eventual convergence of these technologies at their mature stage, culminating in autonomous robotic surgery (ARS). Beyond technical feasibility, deploying such a groundbreaking technology requires careful consideration. Typically, expensive and novel medical advancements are introduced in tertiary academic medical centers, where state-of-the-art infrastructure and trained personnel are available. However, ARS holds the greatest potential for regions lacking access to surgeons, making it crucial to define the optimal scenarios for its implementation. The technical demands of ARS will vary significantly depending on the type of procedure. Decision-making should prioritize a focused set of surgery-requiring conditions and assess the cumulative risk profile of offering ARS in regions with no existing treatment options. Key factors in this evaluation include: 1) procedure commonness; 2) ARS feasibility with current technology; 3) risk of adverse events from a robotic intervention; 4) procedure urgency (i.e., risk of no intervention); 5) risk of abandoning procedure in the setting of technical failure; 6) ability to have remote human oversight; and 7) current availability of resources in the target population/region. Based on these considerations, the initial stabilization of high-energy open skeletal trauma-particularly in active combat military settings-represents a highly feasible and valuable early application. Additionally, the future development of self-sufficient microrobots capable of operating without external imaging could further enhance the portability and accessibility of ARS as the technology matures.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13301 ","pages":"48-62"},"PeriodicalIF":0.0,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12345380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850054","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":"Assessment of generic corrections for multiview Cherenkov emission during total skin electron therapy (TSET).","authors":"Yifeng Zhu, Baozhu Lu, Brook Byrd, Lisha Chen, Emily Xiong, John P Plastaras, Michael LaRiviere, Brian W Pogue, Timothy C Zhu","doi":"10.1117/12.3046423","DOIUrl":"10.1117/12.3046423","url":null,"abstract":"<p><p>Cherenkov imaging is a valuable tool for quality assurance of dose homogeneity in total skin electron therapy (TSET), where patients are treated in six postures using the Stanford technique. Cherenkov signals emitted from the patient's surface are captured, corrected, converted into 2D dose maps, and projected onto the body to generate a 3D pseudo dose distribution. This study aims to improve the accuracy of Cherenkov-converted doses by applying patient-specific generic correction factors derived from Monte Carlo simulations using finite element meshes (FEMs) reconstructed from 3D scans. A clinical study involving eight patients compared the corrected Cherenkov dose with in-vivo dosimetry (IVD) measurements. After applying generic corrections, the Cherenkov-converted dose showed good agreement with IVD, particularly in the shins, with discrepancies within 10%.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13299 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651461","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":"Design of Multiplexed, Live Cell Imaging Experiments Using Excitation Scan-Based Hyperspectral Imaging Microscopy.","authors":"Naga Annamdevula, Rebecca Tang-Holmes, Robert LeDoux, Taylor Jackson, Peyton Baker, Andrea L Britain, Thomas C Rich, Silas J Leavesley","doi":"10.1117/12.3042349","DOIUrl":"https://doi.org/10.1117/12.3042349","url":null,"abstract":"<p><p>In the last 20 years there have been remarkable advances in our ability to track movement and activities of proteins within cells. This is largely due to improved chemical probes and fluorescent proteins, and technical advances in microscopy. A remaining challenge is real-time multiplexed imaging. Excitation scan-based hyperspectral imaging (HSI) approaches are well suited for multiplexed imaging. However, excitation scan-based HSI has not been widely adopted, in part due to a lack of protocols for selection of combinations of fluorescent labels and proteins, and determining the range of excitation wavelengths and dichroic filters. Here we address this issue by outlining considerations for the selection of multiple labels for excitation scan-based HSI. HEK-293 cells were transfected with fluorescent protein constructs and/or loaded with dyes or labels for measurement of excitation spectra. Cells were imaged using a custom-built excitation scan-based HSI microscope that utilizes tunable thin film filters to filter fluorescence excitation from 360 nm to 550 nm in 5 nm increments in conjunction with a long pass dichroic filter and long pass emission filter. We observed that we can effectively quantify the relative abundance and spatial distributions of NucBlue, AlexaFluor 488, AlexaFluor 514, and AlexaFluor 555, Cal520, Cal590, as well as the fluorescent proteins GFP, Cerulean, Turquoise, Venus, tdTomato, and mCherry, individually and in combinations. We are currently assessing the spectra of these fluorophores using excitation scan-based HSI microscope systems.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13323 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026057","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":"Errors Induced by Partial Pathlength Variations Due to Mammographic Compression in Dynamic Diffuse Optical Breast Imaging.","authors":"Kaiser Niknam, Mannu Bardhan Paul, Anthony Donaldson, Mini Das","doi":"10.1117/12.3048012","DOIUrl":"10.1117/12.3048012","url":null,"abstract":"<p><p>Optical imaging methods have the potential to overcome many of the drawbacks posed by current breast imaging modalities. Previous studies have found that mammographic compression induces different hemodynamic effects in cancerous and healthy breast tissue. This effect could be exploited in continuous-wave near-infrared spectroscopic imaging (CW-NIRS) for fast and accurate breast cancer screening. The primary issue with this approach is that breast tissue (and the cancerous mass) is displaced during the compression process, potentially introducing a considerable amount of errors and noise into the NIRS measurements with the current simple models used in estimating blood volume (and/or oxy/deoxy Hb) concentrations. In this work, we examine how these errors change with signal depth with breast compression and investigate methods to correct these based on simulations and experiments.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13314 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766031","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}