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

{"title":"AI-assisted six-dimensional CT imaging for joint instability assessment.","authors":"Chunming Gu, Aeden K Davis, Andrew R Thoreson, Kristin D Zhao, Nirusha Lachman, Yunhong Shu, Francis Baffour, Cynthia H McCollough, Shuai Leng, Hao Gong","doi":"10.1117/12.3087185","DOIUrl":"10.1117/12.3087185","url":null,"abstract":"<p><p>Arthritis can lead to long-term functional disability and morbidity, yet assessing joint instability (i.e., the precursor of osteoarthritis) remains challenging with current standard static imaging methods. To tackle this challenge, we propose artificial intelligence (AI)-assisted six-dimensional CT (6DCT) imaging which integrates 3D spatial data, temporal information, spectral information, and joint kinematics, acquired from photon-counting-detector (PCD) CT. A physics-informed motion correction network (ATOM) was developed to reduce motion artifacts and improve quantitative accuracy in dynamic wrist CT images. Another physics-informed prior-assisted Bayesian network (PMBD) was developed to perform multi-material decomposition and facilitate soft tissue differentiation. Kinematics metrics were derived from motion-corrected bone images to characterize the patterns of joint motion. Patient, cadaver, and phantom scans were used in validation. ATOM reduced motion artifacts (p<0.05; before / after correction): e.g., low-intensity region scores 0.72±0.03 / 0.77±0.03; Structural similarity index 0.94±0.05 / 0.99±0.01. PMBD enhanced material quantification accuracy compared to conventional iterative algorithm: mean-absolute-percent-error PMBD [0.6%, 3.4%], conventional [0.8%, 5%]. Also, PMBD further improved the differentiation between collagen and water, successfully highlighting pathological features in cadaver ligaments. These techniques were further integrated for the downstream kinematic analyses. The proposed 6DCT has the potential to provide accurate visualization and quantitative assessment of dynamic joint pathology.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791529","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":"Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework.","authors":"Chongyu Qu, Allen J Luna, Thomas Z Li, Junchao Zhu, Junlin Guo, Juming Xiong, Kim L Sandler, Bennett A Landman, Yuankai Huo","doi":"10.1117/12.3087567","DOIUrl":"10.1117/12.3087567","url":null,"abstract":"<p><p>Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings-<b>no single model performs best for all cohorts</b>. To address this, we propose personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient by combining cohort-specific knowledge with modern retrieval and reasoning techniques. Given a patient's CT scan and structured metadata-including demographic, clinical, and nodule-level features-the agent <b>first</b> performs cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts to identify the most relevant patient population from a multi-institutional database. <b>Second</b>, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TD-VIT, DLSTM), and multi-modal computer vision based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline-retrieval via FAISS and reasoning via LLM-enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13077649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693995","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":"Noise2Noise Diffusion for Thin-Slice Brain CT Denoising without Clean Training Data.","authors":"Zhennong Chen, Siyeop Yoon, Matthew Tivnan, Junyoung Park, Quanzheng Li, Dufan Wu","doi":"10.1117/12.3085968","DOIUrl":"10.1117/12.3085968","url":null,"abstract":"<p><p>Thin-slice and ultra-high-resolution (UHR) computed tomography (CT) images usually suffer from excessive noise due to limited radiation dose being distributed into small detector units. In the context of deep learning-based image denoising, it is challenging to obtain clean training data from real patients for thin-slice CT because it is unethical to apply excessively high doses to the patients. Supervised learning with noise insertion would face unmatched noise models and possibly domain shift of the training data, which leads to deteriorated model performance. In this work, we proposed a novel method that combined the diffusion model with Noise2Noise, which achieved high-quality noise reduction without requiring clean training data. A conditional denoising diffusion probabilistic model (cDDPM) was trained to sample a CT slice from its two adjacent slices. Because of the noise independence between the input and target, DDPM would sample another noise realization of the target slice. During the inference, the trained DDPM was sampled multiple times to acquire multiple samples of the target slice, which were averaged for a slice with lower noise. The method was validated with simulated thin-slice brain CTs, demonstrating improved quantitative metrics and visual impressions compared to Noise2Noise UNet and supervised DDPM with a slightly mismatched noise model. The mean absolute errors (MAE) of the brain tissues were 4.12, 3.27, and 2.62 for Noise2Noise UNet, supervised DDPM, and the proposed method, respectively. The perceptual loss (LPIPS) was 0.0917, 0.0635, and 0.0422 for the three methods, respectively.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13070551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147679327","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":"Insertion Network for Image Sequence Correspondence Building.","authors":"Dingjie Su, Weixiang Hong, Benoit M Dawant, Bennett A Landman","doi":"10.1117/12.3086579","DOIUrl":"10.1117/12.3086579","url":null,"abstract":"<p><p>We propose a novel method for establishing correspondence between two sequences of 2D images. One particular application of this technique is slice-level content navigation, where the goal is to localize specific 2D slices within a 3D volume or determine the anatomical coverage of a 3D scan based on its 2D slices. This serves as an important preprocessing step for various diagnostic tasks, as well as for automatic registration and segmentation pipelines. Our approach builds sequence correspondence by training a network to learn how to insert a slice from one sequence into the appropriate position in another. This is achieved by encoding contextual representations of each slice and modeling the insertion process using a slice-to-slice attention mechanism. We apply this method to localize manually labeled key slices in body CT scans and compare its performance to the current state-of-the-art alternative known as body part regression, which predicts anatomical position scores for individual slices. Unlike body part regression, which treats each slice independently, our method leverages contextual information from the entire sequence. Experimental results show that the insertion network reduces slice localization errors in supervised settings from 8.4 mm to 5.4 mm, demonstrating a substantial improvement in accuracy.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13196826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019243","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":"DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.","authors":"Chloe Cho, Andrew J McNeil, Bohan Jiang, Adam M Saunders, Gaurav Rudravaram, Yihao Liu, Daniel Moyer, Benoit M Dawant, Bennett A Landman, Eric R Tkaczyk","doi":"10.1117/12.3085989","DOIUrl":"10.1117/12.3085989","url":null,"abstract":"<p><p>Dermatologic imaging has been rapidly expanding, with over 70% of related PubMed articles published since 2016 and over a million images across international research challenges and large-scale datasets with skin images. To improve data quality and usability, standardizing dermatologic imaging data for non-protected health information (non-PHI) research systems is essential. While the International Skin Imaging Collaboration (ISIC) has advanced standards in skin imaging, the field lacks a generalizable infrastructure to organize and describe imaging data for non-PHI research systems. This results in inconsistently labeled, heterogeneous datasets that hinder data integration, scalability, and interoperability. To address this gap, we propose the Dermatology Imaging Data Structure (DermIDS), inspired by the Brain Imaging Data Structure (BIDS) for neuroimaging. This structured framework aims to improve usability across datasets, reveal metadata gaps, and enable scalable artificial intelligence (AI)/machine learning (ML)-ready workflows. To illustrate this system, we curated and processed 1,000,692 images with DermIDS. We demonstrate that DermIDS (1) supports multimodal photographic data acquired from clinical photography, general photography, dermoscopy, reflectance confocal microscopy, and surface 3D imaging; (2) facilitates image-specific technical and clinical metadata organization; and (3) streamlines quality control and harmonization. Across all images, 1,256 unique metadata features were identified. However, 70% of clinical metadata features and 98% of technical metadata features were present in less than 100k images, highlighting key gaps and demonstrating the utility of DermIDS in revealing inconsistencies and opportunities for standardization. Our work supports large-scale analysis and harmonization, laying the foundation for AI/ML-ready workflows to advance dermatologic imaging research.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791543","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":"Assessing the impact of CT reconstruction kernel on radiomic features extracted from normal and fibrotic tissue in patients with diffuse lung disease.","authors":"Spencer H Welland, Andrea Oh, Lila Pourzand, J Webster Stayman, Grace J Gang, Michael F McNitt-Gray, Grace Hyun J Kim","doi":"10.1117/12.3087923","DOIUrl":"10.1117/12.3087923","url":null,"abstract":"<p><strong>Introduction: </strong>Patients with diffuse lung disease (DLD) undergo CT scans for diagnosis and evaluation. Attempts to characterize the radiographic appearance of these regions with quantitative features like radiomics have been hindered by variability in CT acquisition and reconstruction parameters. The purpose of this investigation is to characterize the effect of CT reconstruction kernel on radiomic features in normal and fibrotic regions in DLD patients.</p><p><strong>Methods: </strong>Raw CT projection data of DLD patients receiving a thoracic CT exam was collected from 3 CT scanners (Definition AS, Flash, and Force; Siemens Healthineers, Forchheim, Germany) and retrospectively reconstructed with 5 reconstruction kernels (smooth, medium-smooth, medium, medium-sharp, and sharp). The medium kernel is part of the clinical protocol at our institution and considered the reference kernel for this investigation. Regions of classic normal and classic fibrosis were annotated by 2 thoracic radiologists on images reconstructed with reference kernels. The annotations were copied across each reconstruction, and 72 radiomic features were extracted from each annotation using Pyradiomics. Agreement between features in reference and non-reference kernels was assessed with concordance correlation coefficient (CCC). Features were considered robust if average CCC was > 0.9.</p><p><strong>Results: </strong>There were 66 patients included with 116 regions of normal and 208 regions of fibrosis annotated across all patients. Across kernels in normal tissue, 0/16 GLSZM, 1/16 GLRLM, 0/22 GLCM, and 5/18 first-order features were robust (average CCC > 0.9). Across kernels in fibrotic tissue, 1/16 GLSZM, 3/16 GLRLM, 3/22 GLCM, and 7/18 first-order features were robust, however the effect magnitude of sharper kernels was greater than in normal tissue.</p><p><strong>Conclusion: </strong>First-order features were more robust than other features; GLCM features were the least robust. There are more robust features across kernels in fibrotic tissue, but the magnitude of kernel effect is greater in fibrotic tissue than in normal tissue.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791492","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 Enhancement of Spectral Imaging in Photon-Counting CT via Dynamic Energy Thresholding.","authors":"Donghyeon Lee, Xiao Jiang, J Webster Stayman, Grace J Gang","doi":"10.1117/12.3088128","DOIUrl":"10.1117/12.3088128","url":null,"abstract":"<p><p>Photon-counting computed tomography (PCCT) offers several advantages over conventional spectral systems including improved material separation, reduction of artifacts, and better quantitation. However, imaging performance largely depends on the spectral separation set by energy thresholds, and optimal thresholding can vary by the particular diagnostic task as well as patient anatomy. In most commercial photon-counting detectors (PCDs), the energy thresholds are predefined and fixed, and the capability for dynamic threshold modulation during a scan remains underexplored. In this work, we propose to determine optimal dynamic thresholds that maximize spectral imaging performance given knowledge of patient anatomy from a prior scan or a low-dose 3D scout. Through two-bin PCD simulations under various conditions, we investigated changes in optimal thresholds and assessed potential improvements in dual-energy material decomposition. The simulations include single-pixel decomposition across varying material thicknesses and total photon counts after attenuation, as well as the decomposition of a digital pelvic phantom, which has iodine enhancement in the prostatic arteries. Results demonstrate that the optimal energy threshold changes from 35 to 75 keV in water-calcium decomposition; replacing a fixed threshold with the optimal threshold yields RMSE reductions over 30%, especially in low-count regimes (total photon counts < 100); and pixel-wise threshold modulation in pelvic CT simulations provides 6.9% and 17.2% RMSE reductions for water and iodine, respectively, with reduced streak artifacts. These findings show that dynamic threshold modulation can offer substantial improvements in spectral imaging accuracy for low-dose protocols and challenging anatomical regions when integrated into PCCT systems.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13089854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724673","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":"Diffusion Posterior Sampling for Tomographic Reconstruction with Mixed Resolution Priors.","authors":"Peiqing Teng, J Webster Stayman","doi":"10.1117/12.3088076","DOIUrl":"10.1117/12.3088076","url":null,"abstract":"<p><p>Deep learning has demonstrated an excellent capacity to capture prior information about image classes. This has driven advances in image formation to break through traditional limits of data fidelity in denoising, reconstruction, and processing of undersampled data. Diffusion posterior sampling (DPS) is one approach that combines a generative prior with an analytic model for the measurements to both enforce consistency with measurements and also integrate sophisticated prior information. In such approaches, performance gains can be limited by the quality of the deep learning prior, which, in turn, is limited by the data used to train that network model. In many cases, like tomographic reconstruction, training data is assembled from standard clinical protocols. Specialized, high-fidelity datasets (e.g. very high spatial resolution scans) are often limited in number and/or only target regional anatomy. We propose a novel DPS framework for image reconstruction that uses mixed prior models to enhance regional spatial resolution while maintaining global information for stability and consistency. Specifically, the method integrates a global diffusion model, trained on (untruncated) normal-resolution data, with a regional patch-based diffusion model, trained on high-resolution patches. The prior models are combined using frequency-domain methods, where low-frequency components are extracted from the global model and high-frequency components come from the patch-based model. To address boundary discontinuities inherent to patch-based diffusion model, we adopt a shifted patch division mechanism, which dynamically moves patch boundaries across sampling steps. This strategy removes the stitching artifacts by dispersing them as stochastic noise, while the diffusion prior and posterior constraints gradually eliminate residual inconsistencies. Furthermore, a resampling step is applied after each likelihood update, ensuring stability and preventing error accumulation across iterations. Finally, we introduce a regional sampling scheme, where a binary mask ensures the regional prior is applied within the appropriate anatomy, while the global prior is applied in the background. Experimental results demonstrate that the proposed framework achieves superior reconstruction quality by preserving fine-grained details in regions of interest without sacrificing global structural coherence. This work highlights the potential of combining multi-scale diffusion priors for high-fidelity and efficient posterior sampling in inverse imaging problems.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791579","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":"DeepFixel: Crossing white matter fiber identification through spherical convolutional neural networks.","authors":"Adam M Saunders, Lucas W Remedios, Elyssa M McMaster, Jongyeon Yoon, Gaurav Rudravaram, Adam Sadriddinov, Praitayini Kanakaraj, Bennett A Landman, Adam W Anderson","doi":"10.1117/12.3085833","DOIUrl":"10.1117/12.3085833","url":null,"abstract":"<p><p>Diffusion-weighted magnetic resonance imaging allows for reconstruction of models for structural connectivity in the brain, such as fiber orientation distribution functions (ODFs) that describe the distribution, direction, and volume of white matter fiber bundles in a voxel. Crossing white matter fibers in voxels complicate analysis and can lead to errors in downstream tasks like tractography. We introduce one option for separating fiber ODFs by performing a nonlinear optimization to fit ODFs to the given data and penalizing terms that are not symmetric about the axis of the fiber. However, this optimization is non-convex and computationally infeasible across an entire image (approximately 1.01 × 10⁶ ms per voxel). We introduce DeepFixel, a spherical convolutional neural network approximation for this nonlinear optimization. We model the probability distribution of fibers as a spherical mesh with higher angular resolution than a truncated spherical harmonic representation. To validate DeepFixel, we compare to the nonlinear optimization and a fixel-based separation algorithm of two-fiber and three-fiber ODFs. The median angular correlation coefficient is 1 (interquartile range of 0.00) using the nonlinear optimization algorithm, 0.988 (0.317) using a fiber bundle elements or \"fixel\"-based separation algorithm, and 0.973 (0.004) using DeepFixel. DeepFixel is more computationally efficient than the non-convex optimization (0.32 ms per voxel). DeepFixel's spherical mesh representation is successful at disentangling at smaller angular separations and smaller volume fractions than the fixel-based separation algorithm.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13929 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13196824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019256","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":"Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.","authors":"Connor Baroody, Jinlong Lin, Stefan Wilhelm, Kevin M Dean, Reto Fiolka","doi":"10.1117/12.3085022","DOIUrl":"10.1117/12.3085022","url":null,"abstract":"<p><p>Light-sheet fluorescence microscopy (LSFM) has found numerous applications in the life sciences through its gentle, rapid and efficient volumetric imaging capabilities. It would be advantageous if its rapid 3D imaging capabilities could be extended to non-fluorescent contrast mechanisms such as light-scattering. Yet, the coherent illumination with a light-sheet can cause cross-interference from multiple scatterers. Consequently, this can lead to an object-variant point-spread function, which complicates image analysis. Here we show that by combining axially swept illumination with sparse, but strong scatterers can lead to 3D imaging of comparable quality to traditional fluorescent light-sheet microscopy. We leverage axial sweeping of a light-sheet to both record fluorescence and scattered signals in expanded breast cancer spheroids seeded with gold nanoparticles.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13852 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220723","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}