Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH最新文献_第8页

Impact of Blood Rheological Strategies on the Optimization of Patient-Specific LAAO Configurations for Thrombus Assessment 血液流变学策略对血栓评估患者特异性LAAO配置优化的影响

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-35302-4_50

Carlos Albors, A. Olivares, X. Iriart, H. Cochet, Jordi Mill, Ó. Cámara

引用次数: 0

Long Axis Cardiac MRI Segmentation Using Anatomically-Guided UNets and Transfer Learning 使用解剖引导UNets和迁移学习的长轴心脏MRI分割

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-35302-4_28

Andre Von Zuben, Emily Whitt, F. A. Viana, Luigi E. Perotti

引用次数: 0

In-Silico Analysis of the Influence of Pulmonary Vein Configuration on Left Atrial Haemodynamics and Thrombus Formation in a Large Cohort 大队列肺静脉形态对左心房血流动力学和血栓形成影响的计算机分析

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2022-10-19 DOI: 10.1007/978-3-030-78710-3_58

Jordi Mill, J. Harrison, B. Legghe, A. Olivares, X. Morales, J. Noailly, X. Iriart, H. Cochet, Maxime Sermesant, O. Camara

引用次数: 5

Geometric Deep Learning for the Assessment of Thrombosis Risk in the Left Atrial Appendage 基于几何深度学习的左心耳血栓风险评估

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2022-10-19 DOI: 10.1007/978-3-030-78710-3_61

X. Morales, Jordi Mill, Guillem Simeon, K. A. Juhl, O. D. Backer, R. Paulsen, O. Camara

引用次数: 1

Whole Heart Anatomical Refinement from CCTA Using Extrapolation and Parcellation 利用外推法和分块法对CCTA进行全心解剖细化

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2021-11-18 DOI: 10.1007/978-3-030-78710-3_7

Hao Xu, S. Niederer, Steven E. Williams, D. Newby, M. Williams, A. Young

引用次数: 4

Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks. 使用物理信息神经网络从心内图学习心房纤维取向和电导率张量。

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2021-06-18 DOI: 10.1007/978-3-030-78710-3_62

Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause

{"title":"Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.","authors":"Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause","doi":"10.1007/978-3-030-78710-3_62","DOIUrl":"10.1007/978-3-030-78710-3_62","url":null,"abstract":"Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases and it outperforms a state of the art method in the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.","PeriodicalId":73120,"journal":{"name":"Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH","volume":"2021 ","pages":"650-658"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612271/pdf/EMS140791.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39873578","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}

引用次数: 0

Diffusion biomarkers in chronic myocardial infarction. 慢性心肌梗死中的弥散生物标志物。

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2021-06-01 Epub Date: 2021-06-18 DOI: 10.1007/978-3-030-78710-3_14

Tanjib Rahman, Kévin Moulin, Daniel B Ennis, Luigi E Perotti

{"title":"Diffusion biomarkers in chronic myocardial infarction.","authors":"Tanjib Rahman, Kévin Moulin, Daniel B Ennis, Luigi E Perotti","doi":"10.1007/978-3-030-78710-3_14","DOIUrl":"https://doi.org/10.1007/978-3-030-78710-3_14","url":null,"abstract":"Cardiac diffusion tensor magnetic resonance imaging (cDTI) allows estimating the aggregate cardiomyocyte architecture in healthy subjects and its remodeling as a result of cardiac disease. In this study, cDTI was used to quantify microstructural changes occurring in swine (N=7) six to ten weeks after myocardial infarction. Each heart was extracted and imaged ex vivo with 1mm isotropic spatial resolution. Microstructural changes were quantified in the border zone and infarct region by comparing diffusion tensor invariants - fractional anisotropy (FA), mode, and mean diffusivity (MD) - radial diffusivity, and diffusion tensor eigenvalues with the corresponding values in the remote myocardium. MD and radial diffusivity increased in the infarct and border regions with respect to the remote myocardium (p<0.01). In contrast, FA and mode decreased in the infarct and border regions (p<0.01). Diffusion tensor eigenvalues also increased in the infarct and border regions, with a larger increase in the secondary and tertiary eigenvalues.","PeriodicalId":73120,"journal":{"name":"Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH","volume":" ","pages":"137-147"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476206/pdf/nihms-1734892.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39490950","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}

引用次数: 1

An image registration framework to estimate 3D myocardial strains from cine cardiac MRI in mice. 用图像配准框架估计小鼠电影心脏MRI的三维心肌株。

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2021-06-01 Epub Date: 2021-06-18 DOI: 10.1007/978-3-030-78710-3_27

Maziyar Keshavarzian, Elizabeth Fugate, Saurabh Chavan, Vy Chu, Mohammed Arif, Diana Lindquist, Sakthivel Sadayappan, Reza Avazmohammadi

{"title":"An image registration framework to estimate 3D myocardial strains from cine cardiac MRI in mice.","authors":"Maziyar Keshavarzian, Elizabeth Fugate, Saurabh Chavan, Vy Chu, Mohammed Arif, Diana Lindquist, Sakthivel Sadayappan, Reza Avazmohammadi","doi":"10.1007/978-3-030-78710-3_27","DOIUrl":"https://doi.org/10.1007/978-3-030-78710-3_27","url":null,"abstract":"Accurate and efficient quantification of cardiac motion offers promising biomarkers for non-invasive diagnosis and prognosis of structural heart diseases. Cine cardiac magnetic resonance imaging remains one of the most advanced imaging tools to provide image acquisitions needed to assess and quantify in-vivo heart kinematics. The majority of cardiac motion studies are focused on human data, and there remains a need to develop and implement an image-registration pipeline to quantify full three-dimensional (3D) cardiac motion in mice where ideal image acquisition is challenged by the subject size and heart rate and the possibility of traditional tagged imaging is hampered. In this study, we used diffeomorphic image registration to estimate strains in the left ventricular wall in two wild-type mice and one diabetic mouse. Our pipeline resulted in a continuous and fully 3D strain map over one cardiac cycle. The estimation of 3D regional and transmural variations of strains is a critical step towards identifying mechanistic biomarkers for improved diagnosis and phenotyping of structural left heart diseases including heart failure with reduced or preserved ejection fraction.","PeriodicalId":73120,"journal":{"name":"Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH","volume":" ","pages":"273-284"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276527/pdf/nihms-1706172.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39185081","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}

引用次数: 4

Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI. 用机器学习测量标记MRI中心脏应变的任意点跟踪。

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2021-06-01 Epub Date: 2021-06-18 DOI: 10.1007/978-3-030-78710-3_21

Michael Loecher, Ariel J Hannum, Luigi E Perotti, Daniel B Ennis

{"title":"Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI.","authors":"Michael Loecher, Ariel J Hannum, Luigi E Perotti, Daniel B Ennis","doi":"10.1007/978-3-030-78710-3_21","DOIUrl":"https://doi.org/10.1007/978-3-030-78710-3_21","url":null,"abstract":"Cardiac tagged MR images allow for deformation fields to be measured in the heart by tracking the motion of tag lines throughout the cardiac cycle. Machine learning (ML) algorithms enable accurate and robust tracking of tag lines. Herein, the use of a massive synthetic physics-driven training dataset with known ground truth was used to train an ML network to enable tracking any number of points at arbitrary positions rather than anchored to the tag lines themselves. The tag tracking and strain calculation methods were investigated in a computational deforming cardiac phantom with known (ground truth) strain values. This enabled both tag tracking and strain accuracy to be characterized for a range of image acquisition and tag tracking parameters. The methods were also tested on in vivo volunteer data. Median tracking error was <0.26mm in the computational phantom, and strain measurements were improved in vivo when using the arbitrary point tracking for a standard clinical protocol.","PeriodicalId":73120,"journal":{"name":"Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH","volume":" ","pages":"213-222"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478357/pdf/nihms-1736509.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39495377","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}

引用次数: 2

Optimisation of Left Atrial Feature Tracking Using Retrospective Gated Computed Tomography Images. 利用回顾性门控计算机断层扫描图像优化左心房特征跟踪。

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Pub Date : 2021-06-01 Epub Date: 2021-06-18 DOI: 10.1007/978-3-030-78710-3_8

Charles Sillett, Orod Razeghi, Marina Strocchi, Caroline H Roney, Hugh O'Brien, Daniel B Ennis, Ulrike Haberland, Ronak Rajani, Christopher A Rinaldi, Steven A Niederer

{"title":"Optimisation of Left Atrial Feature Tracking Using Retrospective Gated Computed Tomography Images.","authors":"Charles Sillett, Orod Razeghi, Marina Strocchi, Caroline H Roney, Hugh O'Brien, Daniel B Ennis, Ulrike Haberland, Ronak Rajani, Christopher A Rinaldi, Steven A Niederer","doi":"10.1007/978-3-030-78710-3_8","DOIUrl":"10.1007/978-3-030-78710-3_8","url":null,"abstract":"Retrospective gated cardiac computed tomography (CCT) images can provide high contrast and resolution images of the heart throughout the cardiac cycle. Feature tracking in retrospective CCT images using the temporal sparse free-form deformations (TSFFDs) registration method has previously been optimised for the left ventricle (LV). However, there is limited work on optimising nonrigid registration methods for feature tracking in the left atria (LA). This paper systematically optimises the sparsity weight (SW) and bending energy (BE) as two hyperparameters of the TSFFD method to track the LA endocardium from end-diastole (ED) to end-systole (ES) using 10-frame retrospective gated CCT images. The effect of two different control point (CP) grid resolutions was also investigated. TSFFD optimisation was achieved using the average surface distance (ASD), directed Hausdorff distance (DHD) and Dice score between the registered and ground truth surface meshes and segmentations at ES. For baseline comparison, the configuration optimised for LV feature tracking gave errors across the cohort of 0.826 ± 0.172mm ASD, 5.882 ± 1.524mm DHD, and 0.912 ± 0.033 Dice score. Optimising the SW and BE hyperparameters improved the TSFFD performance in tracking LA features, with case specific optimisations giving errors across the cohort of 0.750 ± 0.144mm ASD, 5.096 ± 1.246mm DHD, and 0.919 ± 0.029 Dice score. Increasing the CP resolution and optimising the SW and BE further improved tracking performance, with case specific optimisation errors of 0.372 ± 0.051mm ASD, 2.739 ± 0.843mm DHD and 0.949 ± 0.018 Dice score across the cohort. We therefore show LA feature tracking using TSFFDs is improved through a chamber-specific optimised configuration.","PeriodicalId":73120,"journal":{"name":"Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH","volume":"12738 ","pages":"71-83"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170531/pdf/nihms-1809986.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9114654","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}

引用次数: 0