Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations

Fanwei Kong; Shawn C. Shadden

doi:10.1007/978-3-030-87202-1_53

Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations

Fanwei Kong
, Shawn C. Shadden

Department of Mechanical Engineering & Materials Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data–a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to the input image data by predicting displacements of multi-resolution control point grids. We discuss the methods of this approach and demonstrate its application to efficiently create simulation-ready whole heart meshes for computational fluid dynamics simulations of the cardiac flow. Our source code is available at https://github.com/fkong7/HeartFFDNet.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
Editors	Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	550-559
Number of pages	10
ISBN (Print)	9783030872014
DOIs	https://doi.org/10.1007/978-3-030-87202-1_53
State	Published - 2021
Event	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: Sep 27 2021 → Oct 1 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12904 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	09/27/21 → 10/1/21

Keywords

Cardiac simulations
Deep learning
Mesh generation

Access to Document

10.1007/978-3-030-87202-1_53

Cite this

Kong, F., & Shadden, S. C. (2021). Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations. In M. de Bruijne, M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings (pp. 550-559). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12904 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87202-1_53

Kong, Fanwei ; Shadden, Shawn C. / Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. editor / Marleen de Bruijne ; Marleen de Bruijne ; Philippe C. Cattin ; Stéphane Cotin ; Nicolas Padoy ; Stefanie Speidel ; Yefeng Zheng ; Caroline Essert. Springer Science and Business Media Deutschland GmbH, 2021. pp. 550-559 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data–a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to the input image data by predicting displacements of multi-resolution control point grids. We discuss the methods of this approach and demonstrate its application to efficiently create simulation-ready whole heart meshes for computational fluid dynamics simulations of the cardiac flow. Our source code is available at https://github.com/fkong7/HeartFFDNet.",

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Kong, F & Shadden, SC 2021, Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations. in M de Bruijne, M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel, Y Zheng & C Essert (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12904 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 550-559, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 09/27/21. https://doi.org/10.1007/978-3-030-87202-1_53

Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations. / Kong, Fanwei; Shadden, Shawn C.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. ed. / Marleen de Bruijne; Marleen de Bruijne; Philippe C. Cattin; Stéphane Cotin; Nicolas Padoy; Stefanie Speidel; Yefeng Zheng; Caroline Essert. Springer Science and Business Media Deutschland GmbH, 2021. p. 550-559 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12904 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data–a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to the input image data by predicting displacements of multi-resolution control point grids. We discuss the methods of this approach and demonstrate its application to efficiently create simulation-ready whole heart meshes for computational fluid dynamics simulations of the cardiac flow. Our source code is available at https://github.com/fkong7/HeartFFDNet.

AB - Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data–a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to the input image data by predicting displacements of multi-resolution control point grids. We discuss the methods of this approach and demonstrate its application to efficiently create simulation-ready whole heart meshes for computational fluid dynamics simulations of the cardiac flow. Our source code is available at https://github.com/fkong7/HeartFFDNet.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings

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A2 - Speidel, Stefanie

A2 - Zheng, Yefeng

A2 - Essert, Caroline

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 27 September 2021 through 1 October 2021

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Kong F, Shadden SC. Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations. In de Bruijne M, de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 550-559. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87202-1_53