Medical image analysis最新文献

{"title":"Individualized mapping of aberrant cortical thickness via stochastic cortical self-reconstruction","authors":"Christian Wachinger ,&nbsp;Dennis M. Hedderich ,&nbsp;Melissa Thalhammer ,&nbsp;Fabian Bongratz","doi":"10.1016/j.media.2025.103788","DOIUrl":"10.1016/j.media.2025.103788","url":null,"abstract":"<div><div>Understanding individual differences in cortical structure is key to advancing diagnostics in neurology and psychiatry. Reference models aid in detecting aberrant cortical thickness, yet site-specific biases limit their direct application to unseen data, and region-wise averages prevent the detection of localized cortical changes. To address these limitations, we developed the Stochastic Cortical Self-Reconstruction (SCSR), a novel method that leverages deep learning to reconstruct cortical thickness maps at the vertex level without needing additional subject information. Trained on over 25,000 healthy individuals, SCSR generates highly individualized cortical reconstructions that can detect subtle thickness deviations. Our evaluations on independent test sets demonstrated that SCSR achieved significantly lower reconstruction errors and identified atrophy patterns that enabled better disease discrimination than established methods. It also hints at cortical thinning in preterm infants that went undetected by existing models, showcasing its versatility. Finally, SCSR excelled in mapping highly resolved cortical deviations of dementia patients from clinical data, highlighting its potential for supporting diagnosis in clinical practice.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103788"},"PeriodicalIF":11.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomical structure-guided joint spatiotemporal graph embedding framework for magnetic resonance fingerprint reconstruction","authors":"Peng Li ,&nbsp;Jianxing Liu ,&nbsp;Yue Hu","doi":"10.1016/j.media.2025.103816","DOIUrl":"10.1016/j.media.2025.103816","url":null,"abstract":"<div><div>Highly undersampled acquisition schemes in magnetic resonance fingerprinting (MRF) typically introduce aliasing artifacts, degrading the accuracy of quantitative imaging. While state-of-the-art graph-based reconstruction methods have shown promise in addressing this challenge by leveraging non-local and non-linear correlations in MRF data, they often face two critical limitations: high computational costs associated with large-scale graph structure estimation and limited capacity to capture complex spatiotemporal dynamics. To overcome these challenges, this study proposes an anatomical structure-guided joint spatiotemporal graph embedding framework for MRF reconstruction. By integrating anatomical segmentation and homogeneity clustering, our framework partitions MRF data into spatially contiguous regions and groups them into clusters based on tissue homogeneity. Subgraphs are then constructed for each cluster, capturing non-local spatial correlations while preserving fine-grained temporal signal dynamics. The hierarchical graph embedding architecture enables efficient focusing on critical correlations, significantly improving reconstruction performance and reducing computational complexity. Numerical experiments on both simulated and <em>in vivo</em> MRF datasets demonstrate that our method outperforms state-of-the-art methods, achieving a <span><math><mo>∼</mo></math></span>2 dB higher signal-to-noise ratio (SNR) in reconstructed data and a <span><math><mo>∼</mo></math></span>70% reduction in reconstruction time. The source code is publicly available at <span><span>https://github.com/bigponglee/SP_GE_MRF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103816"},"PeriodicalIF":11.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-based arbitrary-scale magnetic resonance image super-resolution via progressive k-space reconstruction and denoising","authors":"Jiazhen Wang,&nbsp;Zhihao Shi,&nbsp;Xiang Gu,&nbsp;Yan Yang,&nbsp;Jian Sun","doi":"10.1016/j.media.2025.103814","DOIUrl":"10.1016/j.media.2025.103814","url":null,"abstract":"<div><div>Acquiring high-resolution Magnetic resonance (MR) images is challenging due to constraints such as hardware limitations and acquisition times. Super-resolution (SR) techniques offer a potential solution to enhance MR image quality without changing the magnetic resonance imaging (MRI) hardware. However, typical SR methods are designed for fixed upsampling scales and often produce over-smoothed images that lack fine textures and edge details. To address these issues, we propose a unified diffusion-based framework for arbitrary-scale in-plane MR image SR, dubbed Progressive Reconstruction and Denoising Diffusion Model (PRDDiff). Specifically, the forward diffusion process of PRDDiff gradually masks out high-frequency components and adds Gaussian noise to simulate the downsampling process in MRI. To reverse this process, we propose an Adaptive Resolution Restoration Network (ARRNet), which introduces a current step corresponding to the resolution of input MR image and an ending step corresponding to the target resolution. This design guide the ARRNet to recovering the clean MR image at the target resolution from input MR image. The SR process starts from an MR image at the initial resolution and gradually enhances them to higher resolution by progressively reconstructing high-frequency components and removing the noise based on the recovered MR image from ARRNet. Furthermore, we design a multi-stage SR strategy that incrementally enhances resolution through multiple sequential stages to further improve recovery accuracy. Each stage utilizes a set number of sampling steps from PRDDiff, guided by a specific ending step, to recover details pertinent to the predefined intermediate resolution. We conduct extensive experiments on fastMRI knee dataset, fastMRI brain dataset, our real-collected LR-HR brain dataset, and clinical pediatric cerebral palsy (CP) dataset, including T1-weighted and T2-weighted images for the brain and proton density-weighted images for the knee. The results demonstrate that PRDDiff outperforms previous MR image super-resolution methods in term of reconstruction accuracy, generalization, and downstream lesion segmentation accuracy and CP classification performance. The code is publicly available at <span><span>https://github.com/Jiazhen-Wang/PRDDiff-main</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103814"},"PeriodicalIF":11.8,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GraSTI-ACL: Graph spatial–temporal infomax with adversarial contrastive learning for brain disorders diagnosis based on resting-state fMRI","authors":"Biao He ,&nbsp;Erni Ji ,&nbsp;Xiaofen Zong ,&nbsp;Zhen Liang ,&nbsp;Gan Huang ,&nbsp;Li Zhang","doi":"10.1016/j.media.2025.103815","DOIUrl":"10.1016/j.media.2025.103815","url":null,"abstract":"<div><div>Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in research on brain disorders due to its informative spatial and temporal resolution, and it shows growing potential as a noninvasive tool for assisting clinical diagnosis. Among various methods based on rs-fMRI, graph neural networks have received significant attention because of their inherent structural similarity to functional connectivity networks (FCNs) of the brain. However, constructing FCNs that effectively capture both spatial and temporal information from rs-fMRI remains challenging, as traditional methods often rely on static, fully connected graphs that risk redundancy and neglect dynamic patterns. Based on the information bottleneck principle, this paper proposes a graph augmentation strategy named Graph Spatial–Temporal Infomax (GraSTI) to adaptively preserve both global spatial brain-wide FCNs and local temporal dynamics. We integrate GraSTI with theoretical explanations and design a practical implementation to adapt to our graph augmentation strategy and enhance feature capture capability. Furthermore, GraSTI is incorporated into an adversarial contrastive learning framework to achieve a mutual information equilibrium between graph representation effectiveness and robustness for downstream brain disorders diagnosis tasks. The proposed method is evaluated on datasets from three different brain disorders: Alzheimer’s disease (AD), major depressive disorder (MDD), and bipolar disorder (BD). Extensive experiments demonstrate that the proposed GraSTI-ACL achieves diagnostic accuracy gains of 0.13% to 23.56% for AD, 1.23% to 13.81% for MDD, and 2.53% to 24.53% for BD diagnosis over existing methods. Meanwhile, our method demonstrates strong interpretability in identifying relevant brain regions and connectivities for different brain disorders.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103815"},"PeriodicalIF":11.8,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SuperDiff: A diffusion super-resolution method for digital pathology with comprehensive quality assessment","authors":"Xuan Xu,&nbsp;Saarthak Kapse,&nbsp;Prateek Prasanna","doi":"10.1016/j.media.2025.103808","DOIUrl":"10.1016/j.media.2025.103808","url":null,"abstract":"<div><div>Digital pathology has advanced significantly over the last decade, with Whole Slide Images (WSIs) encompassing vast amounts of data essential for accurate disease diagnosis. High-resolution WSIs are essential for precise diagnosis but technical limitations in scanning equipment and variability in slide preparation can hinder obtaining these images. Super-resolution techniques can enhance low-resolution images; while Generative Adversarial Networks (GANs) have been effective in natural image super-resolution tasks, they often struggle with histopathology due to overfitting and mode collapse. Traditional evaluation metrics fall short in assessing the complex characteristics of histopathology images, necessitating robust histology-specific evaluation methods.</div><div>We introduce SuperDiff, a novel diffusion-based method specially designed for generating and evaluating super-resolution images in digital pathology. It includes a restoration module for histopathology prior and a controllable diffusion module for generating high-quality images. We have curated two histopathology datasets and proposed a comprehensive evaluation strategy which incorporates both full-reference and no-reference metrics to thoroughly assess the quality of digital pathology images.</div><div>Comparative analyses on multiple datasets with state-of-the-art methods reveal that SuperDiff outperforms GANs. Our method offers a versatile solution for histopathology image super-resolution, capable of handling multi-resolution generation from varied input sizes, providing valuable support in diagnostic processes.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103808"},"PeriodicalIF":11.8,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PE-RBNAS: A robust neural architecture search with progressive-enhanced strategies for brain network classification","authors":"Xingyu Wang,&nbsp;Junzhong Ji,&nbsp;Gan Liu,&nbsp;Yadong Xiao","doi":"10.1016/j.media.2025.103813","DOIUrl":"10.1016/j.media.2025.103813","url":null,"abstract":"<div><div>Functional Brain Network (FBN) classification methods based on Neural Architecture Search (NAS) have been increasingly emerging, with their core advantage being the ability to automatically construct high-quality network architectures. However, existing methods exhibit poor robustness when dealing with FBNs that have inherent high-noise characteristics. To address these issues, we propose a robust NAS with progressive-enhanced strategies for FBN classification. Specifically, this method adopts Particle Swarm Optimization as the search method, while treating candidate architectures as individuals, and proposes two progressive-enhanced (PE) strategies to optimize the critical stages of population sampling and fitness evaluation. In the population sampling stage, we first utilize Latin Hypercube Sampling to initialize a small-scale population, ensuring a broad search range. Subsequently, to reduce random fluctuations in searches, we propose a PE supplementary sampling strategy that identifies advantageous regions of the solution space, and performs precise supplementary sampling of the population. In the fitness evaluation stage, to enhance the noise resistance of the searched architectures, we propose a PE fitness evaluation strategy. This strategy first evaluates individual fitness separately using both original data and artificially constructed noise-augmented data, then combines the two fitness scores through a novel progressive formula to determine the final individual fitness. Experiments were conducted on two public datasets: the ABIDE I dataset (1,112 subjects, 17 sites), and ADHD-200 (776 subjects, 8 sites), using AAL/CC200 atlases. Results demonstrate that PE-RBNAS achieves state-of-the-art performance, with 72.61% accuracy on clean ABIDE I data (vs. 71.05% for MC-APSONAS) and 71.82% accuracy under 0.2 noise (vs. 68.15% for PSO-BNAS). The results indicate that, compared to other methods, the proposed method demonstrates better model performance and superior noise resistance.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103813"},"PeriodicalIF":11.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AgGAN: Anatomy-guided generative adversarial network to synthesize arterial spin labeling images for cerebral blood flow measurement under simulated microgravity","authors":"Linkun Cai ,&nbsp;Yawen Liu ,&nbsp;Haijun Niu ,&nbsp;Wei Zheng ,&nbsp;Hao Wang ,&nbsp;Han Lv ,&nbsp;Pengling Ren ,&nbsp;Zhenchang Wang","doi":"10.1016/j.media.2025.103817","DOIUrl":"10.1016/j.media.2025.103817","url":null,"abstract":"<div><div>Microgravity-induced alterations in cerebral blood flow (CBF) may contribute to cognitive decline and neurodegeneration in astronauts. Accurate CBF quantification under microgravity conditions is fundamental for maintaining astronaut health and ensuring the success of human space missions. Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is currently the only non-invasive, non-radioactive technique to quantitatively assessing global and regional CBF. However, deploying MRI scanners aboard space station remains challenging due to technical, logistical and payload limitations. To address this challenge, we propose an end-to-end Anatomy-guided Generative Adversarial Network (AgGAN) as non-invasive, cost-effective, and accurate tool for estimating CBF by synthesizing ASL images under simulated microgravity conditions from corresponding baseline images. Specifically, inspired by radiologists’ diagnostic pattern, we develop a position-aware module to incorporate brain anatomical prior, and a region-adaptive feature extraction module to capture features of irregular brain regions. We also introduce a region-aware focal loss to enhance the synthesis quality of anatomically complex regions. Furthermore, we propose structure boundary-aware loss to encourage the synthesis network to learn boundary details, effectively avoiding exacerbation of partial volume effect and improving the accuracy of CBF quantification. Experimental results demonstrate the superiority of the proposed AgGAN in ASL image synthesis under simulated microgravity and show excellent subjective image quality evaluation. These findings highlight the potential of our model for CBF prediction in astronauts during spaceflight. Our dataset and code are available at <span><span>https://github.com/Cai-Linkun/AgGAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103817"},"PeriodicalIF":11.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145212865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-iterative and uncertainty-aware MRI-based liver fat estimation using an unsupervised deep learning method","authors":"Juan P. Meneses ,&nbsp;Cristian Tejos ,&nbsp;Enes Makalic ,&nbsp;Sergio Uribe","doi":"10.1016/j.media.2025.103811","DOIUrl":"10.1016/j.media.2025.103811","url":null,"abstract":"<div><div>Liver proton density fat fraction (PDFF), the ratio between fat-only and overall proton densities, is an extensively validated biomarker associated with several diseases. In recent years, numerous deep learning-based methods for estimating PDFF have been proposed to optimize acquisition and post-processing times without sacrificing accuracy, compared to conventional methods. However, the lack of interpretability and the often poor generalizability of these DL-based models undermine the adoption of such techniques in clinical practice.</div><div>In this work, we propose an Artificial Intelligence-based Decomposition of water and fat with Echo Asymmetry and Least-squares (AI-DEAL) method, designed to estimate both proton density fat fraction (PDFF) and the associated uncertainty maps. Once trained, AI-DEAL performs a one-shot MRI water-fat separation by first calculating the nonlinear confounder variables, <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow><mrow><mo>∗</mo></mrow></msubsup></math></span> and off-resonance field. It then employs a weighted least squares approach to compute water-only and fat-only signals, along with their corresponding covariance matrix, which are subsequently used to derive the PDFF and its associated uncertainty.</div><div>We validated our method using in vivo liver CSE-MRI, a fat-water phantom, and a numerical phantom. AI-DEAL demonstrated PDFF biases of 0.25% and −0.12% at two liver ROIs, outperforming state-of-the-art deep learning-based techniques. Although trained using in vivo data, our method exhibited PDFF biases of −3.43% in the fat-water phantom and −0.22% in the numerical phantom with no added noise. The latter bias remained approximately constant when noise was introduced. Furthermore, the estimated uncertainties showed good agreement with the observed errors and the variations within each ROI, highlighting their potential value for assessing the reliability of the resulting PDFF maps.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103811"},"PeriodicalIF":11.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term stabilized iris tracking with unsupervised constraints on dynamic AS-OCT","authors":"Lingxi Hu ,&nbsp;Xiao Wu ,&nbsp;Risa Higashita ,&nbsp;Xiaoli Xing ,&nbsp;Menglan Zhou ,&nbsp;Song Lin ,&nbsp;Xiaorong Li ,&nbsp;Yi Yue ,&nbsp;Zunjie Xiao ,&nbsp;Yinglin Zhang ,&nbsp;Chenglin Yao ,&nbsp;Jinming Duan ,&nbsp;Jiang Liu","doi":"10.1016/j.media.2025.103787","DOIUrl":"10.1016/j.media.2025.103787","url":null,"abstract":"<div><div>Primary angle-closure glaucoma (PACG) is responsible for half of all glaucoma-related blindness worldwide. The devastating disease is often clinically silent before causing irreversible visual damage. Glaucomatous optic neuropathy is the major diagnostic criterion for glaucoma. Patients with severe PACG have been clinically found to have significantly lower pupillary reflex velocity and higher iris rigidity. Anterior segment optical coherence tomography (AS-OCT) enables dynamic visualization of the ocular iris anatomy which cannot otherwise be acquired by other imaging modalities. However, automatic quantification of dynamic iris motion on AS-OCT has not yet been implemented. The main challenges lie in the frequent jitter of high-resolution optical imaging, irregular temporal variations of elastic features, and relatively scarce datasets. In this paper, we propose an unsupervised constraint-based jitter refinement tracking (CJRTrack) framework for long-term AS-OCT video tracking. CJRTrack primarily consists of three modules: it first extracts a set of key regions from low-resolution images using an off-the-shelf point tracking algorithm. Given the initialized frames and points, an unsupervised multi-frame differentiable registration network estimates the localized deformation field patch for corresponding high-resolution images. It then refines these predictions using a temporal topology constraint-based module, which explicitly ensures overall trajectory stabilization and tracking. Multi-scale evaluations on two independent AS-OCT datasets demonstrate that CJRTrack significantly outperforms existing tracking models in both accuracy and stability. The clinical adaptivity of the model is further assessed on a glaucoma dataset containing 543 diseased eyes. Jitter-corrected quantification is extracted and used to classify neuropathic damage in primary angle closure patients.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103787"},"PeriodicalIF":11.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recovering intrinsic conduction velocity and action potential duration from electroanatomic mapping data using curvature","authors":"Caroline Roney ,&nbsp;Gernot Plank ,&nbsp;Shohreh Honarbakhsh ,&nbsp;Caterina Vidal Horrach ,&nbsp;Simone Pezzuto ,&nbsp;Edward Vigmond","doi":"10.1016/j.media.2025.103809","DOIUrl":"10.1016/j.media.2025.103809","url":null,"abstract":"<div><div>Electroanatomic mapping systems measure the spread of activation and recovery over the surface of the heart. Propagation in cardiac tissue is complicated by the tissue architecture which produces a spatially varying anisotropic conductivity, leading to complex wavefronts. Curvature of the wavefront is known to affect both conduction velocity (CV) and action potential duration (APD). In this study, we sought to better define the impact of wavefront curvature on these properties, as well as the influence of conductivity, in order to recover intrinsic tissue properties. The dependence of CV and APD on curvature were measured for positive and negative curvatures for several ionic models, and then verified in realistic 2D and 3D simulations. Clinical data were also analysed. Results indicate that the effects of APD and CV are well described by simple formulae, and if the structure of the fibre is known, the intrinsic propagation velocities can be recovered. Geometrical curvature, as determined strictly by wavefront shape and ignoring the fibre structure, leads to large regions of spurious high curvature. This is important for determining pathological zones of slow conduction. In the simulations studied, curvature modulated APD by at most 20 ms.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103809"},"PeriodicalIF":11.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}