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

{"title":"Combined excitation scan-based hyperspectral imaging and digital deconvolution for the assessment of the kinetic and spatial distributions of cAMP signals.","authors":"Naga Annamdevula, Rebecca Tang-Holmes, Robert Ledoux, Taylor Jackson, Anna-Kate Driver, Eliott Franklin, Andrea Britain, Thomas C Rich, Silas J Leavesley","doi":"10.1117/12.3081819","DOIUrl":"10.1117/12.3081819","url":null,"abstract":"<p><p>The cAMP signaling system coordinates a plethora of cellular responses, including contraction, movement, excitability, gene transcription and translation, and Ca²⁺ handling. However, the mechanisms by which cAMP signals differentially regulate specific physiological responses remain elusive. Recent studies from a variety of groups provide compelling evidence that the subcellular location of cAMP signals contributes to signal specificity. Our previous work using a spectral Nikon A1R confocal microscope system highlights the need to consider cAMP signals in three spatial distributions (x,y,z). However, this and other commercially available microscope systems lack sufficient sensitivity and speed to track the kinetics of cAMP signals in four dimensions (4D: x,y,z,t). Here, we propose a combination of excitation scan-based hyperspectral imaging and digital deconvolution approaches to overcome this limitation. Imaging was performed using a custom excitation-scanning hyperspectral microscope based upon an Eclipse Ti2 microscope platform (Nikon Instruments), Titan 300 Xenon arc lamp (Sunoptics), VersaChrome thin-film tunable filters (Semrock Inc.) in a VF-5 tiltable filter wheel (Sutter Instruments), and a Prime 95B sCMOS camera (Photometrics). HEK293 and LNCaP cells were transfected with caDDis cAMP probes (Montana Molecular), and subsequently labeled with MitoTracker and/or NucBlue. Cells were imaged at excitation wavelengths ranging from 360 to 525 nm in 5 nm increments and emission was detected using a 532 nm long pass dichroic filter (Semrock Inc.). Hyperspectral image stacks were acquired at 250-500 nm axial increments (z-stacks). NIS Elements analysis software was used to deconvolve image data at each acquisition wavelength. The contributions from each fluorophore were then assessed using nonnegative linear unmixing. Preliminary results indicate that digital deconvolution of hyperspectral image stacks offers an effective approach to assess the spatial distributions of multiple fluorophores including cAMP probes in 5D (x, y, z, λ, and t). This work was supported by the University of South Alabama Center for Lung Biology, NIH R01HL169522 and S10OD028606, and NSF DBI-2408000.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13857 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13098491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791647","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":"Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models.","authors":"Runchen Wang, Junlin Guo, Siqi Lu, Ruining Deng, Zhengyi Lu, Yanfan Zhu, Yuechen Yang, Chongyu Qu, Yu Wang, Shilin Zhao, Catie Chang, Mitchell Wilkes, Mengmeng Yin, Haichun Yang, Yuankai Huo","doi":"10.1117/12.3086185","DOIUrl":"10.1117/12.3086185","url":null,"abstract":"<p><p>Accurate cell nuclei segmentation is critical for downstream tasks in kidney pathology and remains a major challenge due to the morphological diversity and imaging variability of renal tissues. While our prior work has evaluated early-generation AI cell foundation models in this domain, the effectiveness of recent cell foundation models remains unclear. In this study, we benchmark advanced AI cell foundation models (2025), including CellViT++ variants and Cellpose-SAM, against three widely used cell foundation models developed prior to 2024, using a diverse large-scale set of kidney image patches within a human-in-the-loop rating framework. We further performed fusion-based ensemble evaluation and model agreement analysis to assess the segmentation capabilities of the different models. Our results show that CellViT++ [Virchow] yields the highest standalone performance with 40.3% of predictions rated as \"Good\" on a curated set of 2,091 challenging samples, outperforming all prior models. In addition, our fused model achieves 62.2% \"Good\" predictions and only 0.4% \"Bad\", substantially reducing segmentation errors. Notably, the fusion model (2025) successfully resolved the majority of challenging cases that remained unaddressed in our previous study. These findings demonstrate the potential of AI cell foundation model development in renal pathology and provide a curated dataset of challenging samples to support future kidney-specific model refinement.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13932 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149019177","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":"Modeling a Shared Reality of Tractography through Varied Structural Imaging.","authors":"Trent M Schwartz, Elyssa M McMaster, Gaurav Rudravaram, Chloe Cho, Aravind Krishnan, Michael E Kim, Jessica Samir, Murat Bilgel, Susan Resnick, Lori Beason-Held, Bennett A Landman, Zhiyuan Li","doi":"10.1117/12.3086475","DOIUrl":"10.1117/12.3086475","url":null,"abstract":"<p><p>Though diffusion MRI (dMRI) is the gold standard for white matter tractography, fundamental questions remain about whether captured patterns reflect diffusion-specific phenomena or general structural properties accessible through alternative imaging approaches. This work investigates structural probabilities within the human brain as a complex manifold and examines structural-functional relationships of anatomical bundles to clarify what dMRI specifically captures in white matter architecture. We introduce a framework to extract white matter pathways from FLAIR images without additional subject-specific anatomical context. Using a teacher-student model, we capture systemic information from dMRI-based tractography to guide FLAIR-based tractogram creation. The teacher model trains on dMRI features to generate diffusion tractography, while the student utilizes frozen teacher layers to extract tractography features using only FLAIR input. In our pilot analysis of 14 randomly selected subjects from the Baltimore Longitudinal Study of Aging (BLSA), we performed additional inference on 9 withheld subjects to evaluate robustness. We assessed FLAIR-template generated streamlines using bundle adjacency and Dice coefficient at the voxel level across 39 white matter bundles compared to gold standard diffusion streamlines. Statistical evaluations compared our method against other non-diffusion tractography algorithms using T1-weighted and FLAIR images with subject-specific anatomical context. Results demonstrate our proposed method offers statistically similar performance to other non-diffusion methods when compared to diffusion streamlines These findings suggest that without diffusion data, our method captures unconditional subject-specific prior probabilities of tractography, indicating that tractography patterns may sample from a shared latent space of structural information not unique to any single imaging sequence.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13245530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213640","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":"Illuminating native fluorescence signatures for cancer detection.","authors":"R Tang-Holmes, N Annamdevula, E Franklin, D Chakroborty, M Schuler, T C Rich, N Gong, C Sarkar, S J Leavesley","doi":"10.1117/12.3081028","DOIUrl":"10.1117/12.3081028","url":null,"abstract":"<p><p>Autofluorescence describes light emitted from a naturally occurring substance when exposed to light of a shorter wavelength. It has been shown that cancer-related changes in tissue composition can be responsible for shifts in autofluorescence intensity of biological samples. Several prior studies have worked to characterize the spectral properties of autofluorescent molecules using spectrofluorometers and microscope systems. Here, we quantitatively characterized endogenous fluorophores using spectrofluorometry and fluorescence microscopy with the objective of establishing a reference spectral library for subsequent spectral unmixing of hyperspectral images of mouse tissues during colorectal cancer (CRC) progression. Endogenous fluorophores of interest include collagen, elastin, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), protoporphyrin IX (PPIX), tryptophan, and tyrosine. In the tumor microenvironment (TME), collagen is overproduced and remodeled affecting immune cell infiltration and treatment resistance; elastin degradation generates fragments to promote or inhibit tumor development; NADH and FAD play essential roles in reduction-oxidation reactions; PPIX precedes heme in the heme biosynthesis pathway; and tryptophan and tyrosine are autofluorescent amino acids. Concentration fluctuations of these endogenous fluorophores are directly related to autofluorescent properties which contribute to shifts in bulk autofluorescence spectra concurrent with tissue restructuring caused by the TME. Current hyperspectral fluorescence microscopy techniques utilize emission-scanning hyperspectral imaging (Em-HSI). However, this method requires long acquisition times that would be incompatible with real-time endoscopic screening. Here, we utilize a novel excitation-scanning hyperspectral imaging (Ex-HSI) approach to establish a comprehensive spectral library of several biologically-relevant autofluorescent molecules for downstream spectral unmixing of mouse CRC tissue spectral image stacks.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13855 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635476","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":"LED-based hyperspectral endoscopic imaging: Optical simulation-guided illumination optimization.","authors":"Tianao Li, Naeeme Modir, Baowei Fei","doi":"10.1117/12.3086169","DOIUrl":"https://doi.org/10.1117/12.3086169","url":null,"abstract":"<p><p>Digestive system cancers, particularly esophageal cancer, remain difficult to detect in early stages due to the limited sensitivity of conventional white-light endoscopy. Hyperspectral endoscopic imaging enhances tissue contrast by exploiting wavelength-dependent optical properties. However, its performance depends on efficient and uniform spectral illumination under highly constrained and asymmetric endoscope tip geometries. In practice, the imaging sensor is offset from the center due to the existence of instrument channels and air/water nozzles, complicating LED placement and degrading illumination uniformity. In this work, we present an optical simulation-driven framework for optimizing LED placement in an LED-based, asymmetric hyperspectral endoscopic illumination system. Non-sequential ray tracing and merit-function-based optimization were implemented in Zemax OpticStudio to optimize LED spatial configurations. Two local optimization algorithms-Damped Least Squares (DLS) and Orthogonal Descent (OD)-were systematically evaluated across fifteen wavelength groups. Both methods achieved measurable improvements in irradiance distribution. Overall, OD slightly outperformed DLS, yielding average irradiance improvements of 11.30% on the esophagus wall and 10.15% on the LED field of view relative to the pre-optimization configuration, whereas DLS achieved corresponding average increases of 7.03% and 6.55%, respectively. Rather than prescribing a fixed LED placement pattern, the results provide practical design guidance by demonstrating how adaptive, optimization-driven LED positioning can effectively compensate for asymmetric endoscope geometries while prioritizing different illumination targets. These findings demonstrate that simulation-based LED placement optimization can provide effective guidance for the design of hyperspectral endoscopic illumination systems, high-fidelity modeling, and experimental validation.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13927 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13699329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278411","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":"Wide-range High-precision Eye-tracking based on Purkinje Reflections.","authors":"Anis Idrizović, Ruei Jr-Wu, Michele Rucci, Jannick P Rolland","doi":"10.1117/12.3065468","DOIUrl":"10.1117/12.3065468","url":null,"abstract":"<p><p>Humans continually move their eyes to explore the visual world. Precisely measuring eye movements is, therefore, crucial for both understanding human vision and paving the way for the next generation of augmented and virtual reality devices. Imaging Purkinje reflections enables high-resolution eye-tracking over a limited range. Here we present an optical architecture that enables extension of the tracking range while maintaining 1 arcmin resolution.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13595 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12908036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215168","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":"Improving low-contrast liver metastasis detectability in deep-learning CT denoising using adaptive local fusion driven by total uncertainty and predictive mean.","authors":"Hao Gong, Shravani A Kharat, Shuai Leng, Lifeng Yu, Scott S Hsieh, Joel G Fletcher, Cynthia H McCollough","doi":"10.1117/12.3047080","DOIUrl":"10.1117/12.3047080","url":null,"abstract":"<p><p>Emerging deep-learning-based CT denoising techniques have the potential to improve diagnostic image quality in low-dose CT exams. However, aggressive radiation dose reduction and the intrinsic uncertainty in convolutional neural network (CNN) outputs are detrimental to detecting critical lesions (e.g., liver metastases) in CNN-denoised images. To tackle these issues, we characterized CNN output distribution via total uncertainty (i.e., data + model uncertainties) and predictive mean. Local mean-uncertainty-ratio (MUR) was calculated to detect highly unreliable regions in the denoised images. A MUR-driven adaptive local fusion (ALF) process was developed to adaptively merge local predictive means with the original noisy images, thereby improving image robustness. This process was incorporated into a previously validated deep-learning model observer to quantify liver metastasis detectability, using area under localization receiver operating characteristic curve (LAUC) as the figure-of-merit. For proof-of-concept, the proposed method was established and validated for a ResNet-based CT denoising method. A recent patient abdominal CT dataset was used in validation, involving 3 lesion sizes (7, 9, and 11 mm), 3 lesion contrasts (15, 20, and 25 HU), and 3 dose levels (25%, 50%, and 100% dose). Visual inspection and quantitative analyses were conducted. Statistical significance was tested. Total uncertainty at lesions and liver background generally increased as radiation dose decreased. With fixed dose, lesion-wise MUR showed no dependency on lesion size or contrast, but exhibited large variance across lesion locations (MUR range ~0.7 to 19). Compared to original ResNet-based denoising, the MUR-driven ALF consistently improved lesion detectability in challenging conditions such as lower dose, smaller lesion size, or lower contrast (range of absolute gain in LAUC: 0.04 to 0.1; P-value 0.008). The proposed method has the potential to improve reliability of deep-learning CT denoising and enhance lesion detection.</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/PMC12070600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058608","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":"Fair Text to Medical Image Diffusion Model with Subgroup Distribution Aligned Tuning.","authors":"Xu Han, Fangfang Fan, Jingzhao Rong, Zhen Li, Georges El Fakhri, Qingyu Chen, Xiaofeng Liu","doi":"10.1117/12.3046450","DOIUrl":"10.1117/12.3046450","url":null,"abstract":"<p><p>The Text to Medical Image (T2MedI) approach using latent diffusion models holds significant promise for addressing the scarcity of medical imaging data and elucidating the appearance distribution of lesions corresponding to specific patient status descriptions. Like natural image synthesis models, our investigations reveal that the T2MedI model may exhibit biases towards certain subgroups, potentially neglecting minority groups present in the training dataset. In this study, we initially developed a T2MedI model adapted from the pre-trained Imagen framework. This model employs a fixed Contrastive Language-Image Pre-training (CLIP) text encoder, with its decoder fine-tuned using medical images from the Radiology Objects in Context (ROCO) dataset. We conduct both qualitative and quantitative analyses to examine its gender bias. To address this issue, we propose a subgroup distribution alignment method during fine-tuning on a target application dataset. Specifically, this process involves an alignment loss, guided by an off-the-shelf sensitivity-subgroup classifier, which aims to synchronize the classification probabilities between the generated images and those expected in the target dataset. Additionally, we preserve image quality through a CLIP-consistency regularization term, based on a knowledge distillation framework. For evaluation purposes, we designated the BraTS18 dataset as the target, and developed a gender classifier based on brain magnetic resonance (MR) imaging slices derived from it. Our methodology significantly mitigates gender representation inconsistencies in the generated MR images, aligning them more closely with the gender distribution in the BraTS18 dataset.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13411 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884423","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":"Black-box Optimization of CT Acquisition and Reconstruction Parameters: A Reinforcement Learning Approach.","authors":"David Fenwick, Navid NaderiAlizadeh, Vahid Tarokh, Darin Clark, Jayasai Rajagopal, Anuj Kapadia, Nicholas Felice, Ehsan Samei, Ehsan Abadi","doi":"10.1117/12.3046807","DOIUrl":"10.1117/12.3046807","url":null,"abstract":"<p><p>Protocol optimization is critical in Computed Tomography (CT) for achieving desired diagnostic image quality while minimizing radiation dose. Due to the inter-effect of influencing CT parameters, traditional optimization methods rely on the testing of exhaustive combinations of these parameters. This poses a notable limitation due to the impracticality of exhaustive parameter testing. This study introduces a novel methodology leveraging Virtual Imaging Trials (VITs) and reinforcement learning to more efficiently optimize CT protocols. Computational phantoms with liver lesions were imaged using a validated CT simulator and reconstructed with a novel CT reconstruction Toolkit. The optimization parameter space included tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. The optimization process was done using a Proximal Policy Optimization (PPO) agent which was trained to maximize the Detectability Index (d') of the liver lesion for each reconstructed image. Results showed that our reinforcement learning approach found the absolute maximum d' across the test cases while requiring 79.7% fewer steps compared to an exhaustive search, demonstrating both accuracy and computational efficiency, offering a efficient and robust framework for CT protocol optimization. The flexibility of the proposed technique allows for use of varying image quality metrics as the objective metric to maximize for. Our findings highlight the advantages of combining VIT and reinforcement learning for CT protocol management.</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/PMC12035822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045469","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":"Intra- and inter-scanner CT variability and their impact on diagnostic tasks.","authors":"Isabel Montero, Saman Sotoudeh-Paima, Ehsan Abadi, Ehsan Samei","doi":"10.1117/12.3047016","DOIUrl":"10.1117/12.3047016","url":null,"abstract":"<p><p>The increased development and production of Computed Tomography (CT) scanner technology has expanded patient access to advanced and affordable medical imaging technologies but has also introduced sources of variability in the clinical imaging landscape, which may influence patient care. This study examines the impact of intra-scanner and inter-scanner variability on image quality and quantitative imaging tasks, with a focus on the detectability index (d') as a measure of patient-specific task performance. We evaluated 813 clinical phantom image sets from the COPDGene study, aggregated by CT scanner make, model, and acquisition and reconstruction protocol. Each phantom image set was assessed for image quality metrics, including the Noise Power Spectrum (NPS) and in-plane Modulation Transfer Function (MTF). The d' index was calculated for 12 hypothetical lesion detection tasks, emulating clinically relevant lung and liver lesions of varying sizes and contrast levels. Qualitatively, analysis showed intra-scanner variability in NPS and MTF curves measured for identical acquisition and reconstruction settings. Inter-scanner comparisons demonstrated variability in d' measurements across different scanner makes and models, of similar acquisition and reconstruction settings. The study showed an intra-scanner variability of up to 13.7% and an inter-scanner variability of up to 19.3% in the d' index. These findings emphasize the need for considering scanner variability in patient-centered care and indicate that CT technology may influence the reliability of imaging tasks. The results of this study further motivate the development of virtual scanner models to better model and mitigate the variability observed in the clinical imaging landscape.</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/PMC12035824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037036","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}