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Relevant bibliographies by topics / Computational cardiac atlas

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Academic literature on the topic 'Computational cardiac atlas'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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Journal articles on the topic "Computational cardiac atlas"

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Moosavi, Mir-Hossein, Nasser Fatouraee, Hamid Katoozian, Ali Pashaei, and Alejandro F. Frangi. "USING ATLAS OF HEART SHAPES FOR SIMULATION OF BLOOD FLOW IN LEFT VENTRICLE." Biomedical Engineering: Applications, Basis and Communications 25, no. 06 (2013): 1350050. http://dx.doi.org/10.4015/s1016237213500506.

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Integrative modeling of cardiac system is important for understanding the complex biophysical function of the heart]. To this end, multimodal cardiovascular imaging plays an important role in providing the computational domain, the boundary/initial conditions, and tissue function and properties. In particular, the incorporation of blood flow in the physiological models can help to simulate the hemodynamic properties and their effects on cardiac function. In this paper, we present a multimodal framework for quantitative and subject-specific analysis of blood flow in the cardiac chambers, includ

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Fonseca, Carissa G., Michael Backhaus, David A. Bluemke, et al. "The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart." Bioinformatics 27, no. 16 (2011): 2288–95. http://dx.doi.org/10.1093/bioinformatics/btr360.

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Attard, Mark I., Timothy J. W. Dawes, Antonio de Marvao, et al. "Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging." European Heart Journal - Cardiovascular Imaging 20, no. 6 (2018): 668–76. http://dx.doi.org/10.1093/ehjci/jey175.

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Abstract Aims We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, ha

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Sufian, Md Abu, Lujain Alsadder, Wahiba Hamzi, Sadia Zaman, A. S. M. Sharifuzzaman Sagar, and Boumediene Hamzi. "Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics." Diagnostics 14, no. 23 (2024): 2675. http://dx.doi.org/10.3390/diagnostics14232675.

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Background/Objectives: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. Methods: The research utilised quantitative 3D/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project’s (CAP) o

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Gonzalez, Gerardo, David Warriner, Tom Jackson, et al. "Abstract 16108: Adverse Left Ventricular Remodelling Patterns Predict Response to Cardiac Resynchronization Therapy." Circulation 132, suppl_3 (2015). http://dx.doi.org/10.1161/circ.132.suppl_3.16108.

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Introduction: The anatomy of the left ventricle (LV) remodels in heart failure. In this work we test the hypothesis that pre-implant LV shape can predict response to cardiac resynchronization therapy (CRT). Methods: The anatomy of the LV of 50 subjects selected for CRT was acquired and manually segmented from magnetic resonance imaging acquired using a steady-state free-precision 3D sequence. A computational atlas was built from 3D meshes that were fitted to the resulting segmentations, and was used to obtain atlas-based shape metrics from the cohort. Segmentation and 3D meshing was repeated i

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Ma, Hong, Ziqing Liu, Yuchen Yang, et al. "Abstract 225: Coordinated Transcriptome and Cell State Dynamics of Non-myocytes in Heart Regeneration." Circulation Research 127, Suppl_1 (2020). http://dx.doi.org/10.1161/res.127.suppl_1.225.

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Cardiac regeneration occurs primarily through proliferation of existing cardiomyocytes, yet the regenerative response also involves complex interactions between distinct cardiac cell types including not only cardiomyocytes, but also non-cardiomyocytes (nonCMs). However, the subpopulations, distinguishing molecular features, cellular functions, and intercellular interactions of nonCMs in heart regeneration remain largely unexplored. Using the LIGER algorithm, we assembled an atlas of cell states from 61,977 individual nonCM scRNA-seq profiles isolated at multiple time points during heart regene

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Govil, Sachin, Brendan T. Crabb, Yu Deng, et al. "A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot." Journal of Cardiovascular Magnetic Resonance 25, no. 1 (2023). http://dx.doi.org/10.1186/s12968-023-00924-1.

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Abstract Background Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardia

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Cheng, F., E. Rauseo, S. Misghina, et al. "Biventricular modelling for investigating ventricular arrhythmias in silico." European Heart Journal 45, Supplement_1 (2024). http://dx.doi.org/10.1093/eurheartj/ehae666.3501.

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Abstract Background Ventricular arrhythmias have a significant incidence rate worldwide and may potentially lead to sudden cardiac death in severe cases. Despite therapeutic advancements, the intricate mechanisms dictating these arrhythmias remain elusive. Biventricular modelling and deep learning provide a way to investigate the complex electrophysiological substrates underlying cardiac arrhythmogenesis. Purpose This study aims to develop and validate a patient-specific biventricular computational modelling pipeline for simulating ventricular arrhythmias in silico. We provide a comprehensive

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Biffi, Carlo, Marvao Antonio de, Mark I. Attard, et al. "Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework." July 25, 2017. https://doi.org/10.5281/zenodo.834610.

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This repository contains clinical and imaging data used in the paper: C. Biffi, A. de Marvao, M. I. Attard, T. J. W. Dawes, N. Whiffin, W. Bai, W. Shi, C. Francis, H. Meyer, R. Buchan, S. A. Cook, D. Rueckert, D. P. O'Regan, Three-dimensional cardiovascular imaging-genetics: a mass univariate framework. Bioinformatics 34, 97-103 (2018). Regarding the repository content: <em>clinicalData.RDS</em> is a 1124x11 matrix which contains in its columns the gender, age, body surface area, systolic blood pressure data of the 1124 healthy caucasian subjects studied in the paper. The last six col

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Mohamed, A., P. Lamata, W. Williamson, et al. "Right ventricular morphology and function analysis in moderately preterm-born young adults." European Heart Journal 41, Supplement_2 (2020). http://dx.doi.org/10.1093/ehjci/ehaa946.0303.

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Abstract Background Previous studies have shown that prematurity leads to altered right ventricular (RV) geometry and performance with persistent impairments in RV systolic function in young adulthood. It is unknown to what extent pulmonary physiology impacts these findings. Purpose To better quantify known alterations in RV morphology and function in preterm-born young adults and to determine to what extent these changes are influenced by the pulmonary circulation. Methods A total of 101 normotensive preterm-born (n=47, mean gestational age 32.8±3.2 weeks) and term-born (n=54) young adults we

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Book chapters on the topic "Computational cardiac atlas"

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Fonseca, Carissa G., Michael Backhaus, Jae Do Chung, et al. "The Cardiac Atlas Project: Rationale, Design and Procedures." In Statistical Atlases and Computational Models of the Heart. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15835-3_4.

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Backhaus, Michael, Randall Britten, Jae Do Chung, et al. "The Cardiac Atlas Project: Development of a Framework Integrating Cardiac Images and Models." In Statistical Atlases and Computational Models of the Heart. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15835-3_6.

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De Craene, Mathieu, Federico M. Sukno, Catalina Tobon-Gomez, et al. "Atlas Construction and Image Analysis Using Statistical Cardiac Models." In Statistical Atlases and Computational Models of the Heart. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15835-3_1.

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Lombaert, Hervé, Jean-Marc Peyrat, Laurent Fanton, et al. "Statistical Atlas of Human Cardiac Fibers: Comparison with Abnormal Hearts." In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28326-0_21.

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Medrano-Gracia, Pau, Avan Suinesiaputra, Brett Cowan, et al. "An Atlas for Cardiac MRI Regional Wall Motion and Infarct Scoring." In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36961-2_22.

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Sinclair, Matthew, Devis Peressutti, Esther Puyol-Antón, et al. "Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas." In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52718-5_7.

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Medrano-Gracia, Pau, Brett R. Cowan, J. Paul Finn, et al. "The Cardiac Atlas Project: Preliminary Description of Heart Shape in Patients with Myocardial Infarction." In Statistical Atlases and Computational Models of the Heart. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15835-3_5.

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Dangi, Shusil, Nathan Cahill, and Cristian A. Linte. "Integrating Atlas and Graph Cut Methods for Left Ventricle Segmentation from Cardiac Cine MRI." In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52718-5_9.

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Baličević, Vedrana, Hrvoje Kalinić, Sven Lončarić, et al. "Atlas Construction for Cardiac Velocity Profiles Segmentation Using a Lumped Computational Model of Circulatory System." In Functional Imaging and Modeling of the Heart. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38899-6_11.

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Puyol-Antón, Esther, Bram Ruijsink, Hélène Langet, et al. "Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas." In Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12029-0_11.

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Conference papers on the topic "Computational cardiac atlas"

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Hoogendoorn, Corné, Tristan Whitmarsh, Nicolas Duchateau, Federico M. Sukno, Mathieu De Craene, and Alejandro F. Frangi. "A groupwise mutual information metric for cost efficient selection of a suitable reference in cardiac computational atlas construction." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844428.

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Tenhoff, Amanda C., Alex J. Deakyne, Tinen L. Iles, et al. "Development of an Open-Access Library of Pediatric Congenital Heart Diseases and Treatments: A Tutorial on the Atlas of Human Cardiac Anatomy." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9064.

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Abstract The major aim of this project is to construct a growing database of information regarding specific manifestations of congenital heart diseases (CHDs), subsequent treatments, clinical cases, and patient outcomes. This will include 3D models generated from clinical imaging of individual patient hearts and respective de-identified clinical case information — all of which will be incorporated onto the free-access Atlas of Human Cardiac Anatomy website (http://www.vhlab.umn.edu/atlas/), where anyone can learn more about these diseases and their complexities [1]. Generated models can also b

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Zhuang, Xiahai, Wenzhe Shi, Haiyan Wang, Daniel Rueckert, and Sebastien Ourselin. "Computation on shape manifold for atlas generation: application to whole heart segmentation of cardiac MRI." In SPIE Medical Imaging, edited by Sebastien Ourselin and David R. Haynor. SPIE, 2013. http://dx.doi.org/10.1117/12.2007181.

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