Medical image analysis最新文献

{"title":"Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks","authors":"Jingjing Gao ,&nbsp;Mingqi Liu ,&nbsp;Maomin Qian ,&nbsp;Heping Tang ,&nbsp;Junyi Wang ,&nbsp;Liang Ma ,&nbsp;Yanling Li ,&nbsp;Xin Dai ,&nbsp;Zhengning Wang ,&nbsp;Fengmei Lu ,&nbsp;Fan Zhang","doi":"10.1016/j.media.2025.103482","DOIUrl":"10.1016/j.media.2025.103482","url":null,"abstract":"<div><div>The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity for precise striatal segmentation. In this study, we introduce a novel deep clustering pipeline for automated, fine-scale parcellation of the striatum using diffusion MRI (dMRI) tractography. Initially, we employ a voxel-based probabilistic fiber tractography algorithm combined with a fiber-tract embedding technique to capture intricate dMRI connectivity patterns. To maintain critical inter-voxel relationships, our approach employs Graph Neural Networks (GNNs) to create accurate graph representations of the striatum. This involves encoding probabilistic fiber bundle characteristics as node attributes and refining edge weights using activation functions to enhance the graph’s interpretability and accuracy. The methodology incorporates a Transformer-based GraphConv autoencoder in the pre-training phase to extract critical spatial features while minimizing reconstruction loss. In the fine-tuning phase, a novel joint loss mechanism markedly improves segmentation precision and anatomical fidelity. Integration of traditional clustering techniques with multi-head self-attention mechanisms further elevates the accuracy and robustness of our segmentation approach. This methodology provides new insights into the striatum’s role in cognition and behavior and offers potential clinical applications for neurological disorders.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103482"},"PeriodicalIF":10.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402682","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":"Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction","authors":"Peng Li,&nbsp;Yue Hu","doi":"10.1016/j.media.2025.103481","DOIUrl":"10.1016/j.media.2025.103481","url":null,"abstract":"<div><div>Magnetic resonance fingerprinting (MRF) is a promising technique for fast quantitative imaging of multiple tissue parameters. However, the highly undersampled schemes utilized in MRF typically lead to noticeable aliasing artifacts in reconstructed images. Existing model-based methods can mitigate aliasing artifacts and enhance reconstruction quality but suffer from long reconstruction times. In addition, data priors used in these methods, such as low-rank and total variation, make it challenging to incorporate non-local and non-linear redundancies in MRF data. Furthermore, existing deep learning-based methods for MRF often lack interpretability and struggle with the high computational overhead caused by the high dimensionality of MRF data. To address these issues, we introduce a novel deep graph embedding framework based on the Laplacian eigenmaps for improved MRF reconstruction. Our work first models the acquired high-dimensional MRF data and the corresponding parameter maps as graph data nodes. Then, we propose an MRF reconstruction framework based on the graph embedding framework, retaining intrinsic graph structures between parameter maps and MRF data. To improve the accuracy of the estimated graph structure and the computational efficiency of the proposed framework, we unroll the iterative optimization process into a deep neural network, incorporating a learned graph embedding module to adaptively learn the Laplacian eigenmaps. By introducing the graph embedding framework into the MRF reconstruction, the proposed method can effectively exploit non-local and non-linear correlations in MRF data. Numerical experiments demonstrate that our approach can reconstruct high-quality MRF data and multiple parameter maps within a significantly reduced computational cost.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103481"},"PeriodicalIF":10.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350431","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":"Second order kinematic surface fitting in anatomical structures","authors":"Wilhelm Wimmer ,&nbsp;Hervé Delingette","doi":"10.1016/j.media.2025.103488","DOIUrl":"10.1016/j.media.2025.103488","url":null,"abstract":"<div><div>Symmetry detection and morphological classification of anatomical structures play pivotal roles in medical image analysis. The application of kinematic surface fitting, a method for characterizing shapes through parametric stationary velocity fields, has shown promising results in computer vision and computer-aided design. However, existing research has predominantly focused on first order rotational velocity fields, which may not adequately capture the intricate curved and twisted nature of anatomical structures. To address this limitation, we propose an innovative approach utilizing a second order velocity field for kinematic surface fitting. This advancement accommodates higher rotational shape complexity and improves the accuracy of symmetry detection in anatomical structures. We introduce a robust fitting technique and validate its performance through testing on synthetic shapes and real anatomical structures. Our method not only enables the detection of curved rotational symmetries (<em>core lines</em>) but also facilitates morphological classification by deriving intrinsic shape parameters related to curvature and torsion. We illustrate the usefulness of our technique by categorizing the shape of human cochleae in terms of the intrinsic velocity field parameters. The results showcase the potential of our method as a valuable tool for medical image analysis, contributing to the assessment of complex anatomical shapes.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103488"},"PeriodicalIF":10.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SegmentAnyBone: A universal model that segments any bone at any location on MRI","authors":"Hanxue Gu ,&nbsp;Roy Colglazier ,&nbsp;Haoyu Dong ,&nbsp;Jikai Zhang ,&nbsp;Yaqian Chen ,&nbsp;Zafer Yildiz ,&nbsp;Yuwen Chen ,&nbsp;Lin Li ,&nbsp;Jichen Yang ,&nbsp;Jay Willhite ,&nbsp;Alex M. Meyer ,&nbsp;Brian Guo ,&nbsp;Yashvi Atul Shah ,&nbsp;Emily Luo ,&nbsp;Shipra Rajput ,&nbsp;Sally Kuehn ,&nbsp;Clark Bulleit ,&nbsp;Kevin A. Wu ,&nbsp;Jisoo Lee ,&nbsp;Brandon Ramirez ,&nbsp;Maciej A. Mazurowski","doi":"10.1016/j.media.2025.103469","DOIUrl":"10.1016/j.media.2025.103469","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of the MRIs into different organs and tissues would be very beneficial as it would allow more accurate measurements, which are essential for accurate diagnosis and effective treatment planning. Specifically, segmenting bones in MRI would allow for more quantitative assessments of musculoskeletal conditions, while such assessments are largely absent in current radiological practice. The difficulty of bone MRI segmentation is illustrated by the fact that limited algorithms are publicly available, and those contained in the literature typically address a specific anatomic area. In our study, we propose a versatile, publicly available deep learning model for bone segmentation in MRI at multiple standard MRI locations. The proposed model can operate in two modes: fully automated segmentation and prompt-based segmentation. Our contributions include (1) collecting and annotating a new MRI dataset across various MRI protocols, encompassing 320 annotated volumes and more than 10k annotated slices across diverse anatomic regions; (2) investigating several standard network architectures and strategies for automated segmentation; (3) introducing SegmentAnyBone, an innovative foundation model-based approach that extends the Segment Anything Model (SAM); (4) comparative analysis of our algorithm and previous approaches; and (5) generalization analysis of our algorithm across different anatomical locations and MRI sequences, as well as three external datasets. We publicly release our model at <span><span>Github Code</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103469"},"PeriodicalIF":10.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436449","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":"Imaging the structural connectome with hybrid MRI-microscopy tractography","authors":"Silei Zhu ,&nbsp;Istvan N. Huszar ,&nbsp;Michiel Cottaar ,&nbsp;Greg Daubney ,&nbsp;Nicole Eichert ,&nbsp;Taylor Hanayik ,&nbsp;Alexandre A. Khrapitchev ,&nbsp;Rogier B. Mars ,&nbsp;Jeroen Mollink ,&nbsp;Jerome Sallet ,&nbsp;Connor Scott ,&nbsp;Adele Smart ,&nbsp;Saad Jbabdi ,&nbsp;Karla L. Miller ,&nbsp;Amy F.D. Howard","doi":"10.1016/j.media.2025.103498","DOIUrl":"10.1016/j.media.2025.103498","url":null,"abstract":"<div><div>Mapping how neurons are structurally wired into whole-brain networks can be challenging, particularly in larger brains where 3D microscopy is not available. Multi-modal datasets combining MRI and microscopy provide a solution, where high resolution but 2D microscopy can be complemented by whole-brain but lowresolution MRI. However, there lacks unified approaches to integrate and jointly analyse these multi-modal data in an insightful way. To address this gap, we introduce a data-fusion method for hybrid MRI-microscopy fibre orientation and connectome reconstruction. Specifically, we complement precise “in-plane” orientations from microscopy with “through-plane” information from MRI to construct 3D hybrid fibre orientations at resolutions far exceeding that of MRI whilst preserving microscopy's myelin specificity, resulting in superior fibre tracking. Our method is openly available, can be deployed on standard 2D microscopy, including different microscopy contrasts, and is species agnostic, facilitating neuroanatomical investigation in both animal models and human brains.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103498"},"PeriodicalIF":10.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023","authors":"Jun Lyu ,&nbsp;Chen Qin ,&nbsp;Shuo Wang ,&nbsp;Fanwen Wang ,&nbsp;Yan Li ,&nbsp;Zi Wang ,&nbsp;Kunyuan Guo ,&nbsp;Cheng Ouyang ,&nbsp;Michael Tänzer ,&nbsp;Meng Liu ,&nbsp;Longyu Sun ,&nbsp;Mengting Sun ,&nbsp;Qing Li ,&nbsp;Zhang Shi ,&nbsp;Sha Hua ,&nbsp;Hao Li ,&nbsp;Zhensen Chen ,&nbsp;Zhenlin Zhang ,&nbsp;Bingyu Xin ,&nbsp;Dimitris N. Metaxas ,&nbsp;Chengyan Wang","doi":"10.1016/j.media.2025.103485","DOIUrl":"10.1016/j.media.2025.103485","url":null,"abstract":"<div><div>Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial–temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103485"},"PeriodicalIF":10.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386449","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":"Large vessel occlusion identification network with vessel guidance and asymmetry learning on CT angiography of acute ischemic stroke patients","authors":"Hulin Kuang ,&nbsp;Xinyuan Liu ,&nbsp;Jin Liu ,&nbsp;Shulin Liu ,&nbsp;Shuai Yang ,&nbsp;Weihua Liao ,&nbsp;Wu Qiu ,&nbsp;Guanghua Luo ,&nbsp;Jianxin Wang","doi":"10.1016/j.media.2025.103490","DOIUrl":"10.1016/j.media.2025.103490","url":null,"abstract":"<div><div>Identifying large vessel occlusion (LVO) is of significant importance for the treatment and prognosis of acute ischemic stroke (AIS) patients. CT Angiography (CTA) is commonly used in LVO identification due to its visibility of vessels and short acquisition time. It is challenging to make LVO identification methods focus on vascular regions without vessel segmentation while accurate vessel segmentation is difficult and takes more time. Meanwhile, most existing methods fail to effectively integrate clinical prior knowledge. In this work, we propose VANet, a novel LVO identification network which utilizes coarse-grained vessel feature for feature enhancement and learns asymmetry of two brain hemispheres on CTA of AIS patients. Firstly, we reconstruct 3D CTA scans into 2D based on maximum intensity projection (MIP) to reduce computational complexity and highlight vessel information. Secondly, we design a coarse-grained vessel aware module based on simple edge detection and morphological operations to acquire coarse-grained vessel feature without precise vessel segmentation. Thirdly, we design a vessel-guided feature enhancement that directs the model’s attention to vessel areas in the images by utilizing coarse-grained vessel feature. Finally, inspired by the clinical knowledge that LVO can lead to asymmetry in brain, we design an asymmetry learning module utilizing deep asymmetry supervision to keep the patients’ inherent asymmetry invariant and using asymmetry computing to acquire effective asymmetry features. We validate the proposed VANet on our private internal and external AIS-LVO datasets which contain 366 and 81 AIS patients, respectively. The results indicate that our proposed VANet achieves an accuracy of 94.54% and an AUC of 0.9685 on the internal dataset, outperforms 11 state-of-the-art methods (including general classification methods and LVO-specific methods). Besides, our method also achieves the best accuracy of 88.89% and AUC of 0.9111 when compared to 11 methods on the external test dataset, implying its good generalization ability. Interpretability analysis shows that the proposed VANet can effectively focus on vascular regions and learn asymmetry features.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103490"},"PeriodicalIF":10.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376588","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":"ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases","authors":"Cynthia Xinran Li ,&nbsp;Indrani Bhattacharya ,&nbsp;Sulaiman Vesal ,&nbsp;Pejman Ghanouni ,&nbsp;Hassan Jahanandish ,&nbsp;Richard E. Fan ,&nbsp;Geoffrey A. Sonn ,&nbsp;Mirabela Rusu","doi":"10.1016/j.media.2025.103486","DOIUrl":"10.1016/j.media.2025.103486","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) is increasingly being used to detect prostate cancer, yet its interpretation can be challenging due to subtle differences between benign and cancerous tissue. Recently, Denoising Diffusion Probabilistic Models (DDPMs) have shown great utility for medical image segmentation, modeling the process as noise removal in standard Gaussian distributions. In this study, we further enhance DDPMs by introducing the knowledge that the occurrence of cancer varies across the prostate (e.g., <span><math><mo>∼</mo></math></span>70% of prostate cancers occur in the peripheral zone). We quantify such heterogeneity with a registration pipeline to calculate voxel-level cancer distribution mean and variances. Our proposed approach, ProstAtlasDiff, relies on DDPMs that use the cancer atlas to model noise removal and segment cancer on MRI. We trained and evaluated the performance of ProstAtlasDiff in detecting clinically significant cancer in a multi-institution multi-scanner dataset, and compared it with alternative models. In a lesion-level evaluation, ProstAtlasDiff achieved statistically significantly higher accuracy (0.91 vs. 0.85, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>), specificity (0.91 vs. 0.84, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>), positive predictive value (PPV, 0.50 vs. 0.35, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>), compared to alternative models. ProstAtlasDiff also offers more accurate cancer outlines, achieving a higher Dice Coefficient (0.33 vs. 0.31, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>). Furthermore, we evaluated ProstAtlasDiff in an independent cohort of 91 patients who underwent radical prostatectomy to compare its performance to that of radiologists, relative to whole-mount histopathology ground truth. ProstAtlasDiff detected 16% (15 lesions out of 93) more clinically significant cancers compared to radiologists (sensitivity: 0.90 vs. 0.75, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>), and was comparable in terms of ROC-AUC, PR-AUC, PPV, accuracy, and Dice coefficient (<span><math><mrow><mi>p</mi><mo>≥</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>). Furthermore, we evaluated ProstAtlasDiff in a second independent cohort of 537 subjects and observed that ProsAtlasDiff outperformed alternative approaches. These results suggest that ProstAltasDiff has the potential to assist in localizing cancer for biopsy guidance and treatment planning.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103486"},"PeriodicalIF":10.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428901","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":"Multitask learning in minimally invasive surgical vision: A review","authors":"Oluwatosin Alabi ,&nbsp;Tom Vercauteren ,&nbsp;Miaojing Shi","doi":"10.1016/j.media.2025.103480","DOIUrl":"10.1016/j.media.2025.103480","url":null,"abstract":"<div><div>Minimally invasive surgery (MIS) has revolutionized many procedures and led to reduced recovery time and risk of patient injury. However, MIS poses additional complexity and burden on surgical teams. Data-driven surgical vision algorithms are thought to be key building blocks in the development of future MIS systems with improved autonomy. Recent advancements in machine learning and computer vision have led to successful applications in analysing videos obtained from MIS with the promise of alleviating challenges in MIS videos.</div><div>Surgical scene and action understanding encompasses multiple related tasks that, when solved individually, can be memory-intensive, inefficient, and fail to capture task relationships. Multitask learning (MTL), a learning paradigm that leverages information from multiple related tasks to improve performance and aid generalization, is well-suited for fine-grained and high-level understanding of MIS data.</div><div>This review provides a narrative overview of the current state-of-the-art MTL systems that leverage videos obtained from MIS. Beyond listing published approaches, we discuss the benefits and limitations of these MTL systems. Moreover, this manuscript presents an analysis of the literature for various application fields of MTL in MIS, including those with large models, highlighting notable trends, new directions of research, and developments.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103480"},"PeriodicalIF":10.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378848","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":"Implementation of a Cellular Automaton for efficient simulations of atrial arrhythmias","authors":"Giada S. Romitti ,&nbsp;Alejandro Liberos ,&nbsp;María Termenón-Rivas ,&nbsp;Javier Barrios-Álvarez de Arcaya ,&nbsp;Dolors Serra ,&nbsp;Pau Romero ,&nbsp;David Calvo ,&nbsp;Miguel Lozano ,&nbsp;Ignacio García-Fernández ,&nbsp;Rafael Sebastian ,&nbsp;Miguel Rodrigo","doi":"10.1016/j.media.2025.103484","DOIUrl":"10.1016/j.media.2025.103484","url":null,"abstract":"<div><div>In silico models offer a promising advancement for studying cardiac arrhythmias and their clinical implications. However, existing detailed mathematical models often suffer from prolonged computational time compared to diagnostic needs. This study introduces a Cellular Automaton (CA) model tailored to replicate atrial electrophysiology in different stages of Atrial Fibrillation (AF), including persistent AF (PsAF).</div><div>The CA, using a finite set of states, has been trained using biophysical simulations on a reduced domain for a large set of pacing conditions. Fine-tuning included tissue heterogeneity and anisotropic propagation through pacing simulations. Characterized by Action Potential Duration (APD), Diastolic Interval (DI) and Conduction Velocity (CV) for varying levels of electrical remodeling, the biophysical simulations introduced restitution curves or surfaces into the CA. Validation involved a comprehensive comparison with realistic 2D and 3D atrial models, evaluating healthy and pro-arrhythmic behaviors. Comparisons between CA and biophysical solver revealed striking proximity, with a Cycle Length difference of <span><math><mo>&lt;</mo></math></span>10 ms in self-sustained re-entry and a <span><math><mrow><mn>4</mn><mo>.</mo><mn>66</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>57</mn></mrow></math></span> ms difference in depolarization times across the complete atrial geometry. Notably, the CA model exhibited a 80% accuracy, 96% specificity and 45% sensitivity in predicting AF inducibility under different pacing sites and substrate conditions. Additionally, the CA allowed for a 64-fold decrease in computing time compared to the biophysical solver.</div><div>CA emerges as an efficient and valid model for simulation of atrial electrophysiology across different stages of AF, with potential as a general screening tool for rapid tests. While biophysical tests are recommended for investigating specific mechanisms, CA proves valuable in clinical applications for personalized therapy planning through digital twin simulations.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103484"},"PeriodicalIF":10.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}