Multi-modality cardiac image computing: a survey
Multi-modality cardiac image computing: a survey
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.
Cardiac, Fusion, Multi-modality imaging, Registration, Review, Segmentation
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Ding, Wangbin
ce6c8e72-9208-49fc-b79d-780d4293bc15
Huang, Liqin
4565d61e-d3eb-4ffd-97af-731ae41e5335
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Grau, Vicente
c6992187-7840-45ce-93a0-d54d06031c7d
27 June 2023
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Ding, Wangbin
ce6c8e72-9208-49fc-b79d-780d4293bc15
Huang, Liqin
4565d61e-d3eb-4ffd-97af-731ae41e5335
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Grau, Vicente
c6992187-7840-45ce-93a0-d54d06031c7d
Li, Lei, Ding, Wangbin and Huang, Liqin
,
et al.
(2023)
Multi-modality cardiac image computing: a survey.
Medical Image Analysis, 88, [102869].
(doi:10.1016/j.media.2023.102869).
Abstract
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.
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Accepted/In Press date: 12 June 2023
e-pub ahead of print date: 16 June 2023
Published date: 27 June 2023
Keywords:
Cardiac, Fusion, Multi-modality imaging, Registration, Review, Segmentation
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Local EPrints ID: 488809
URI: http://eprints.soton.ac.uk/id/eprint/488809
ISSN: 1361-8415
PURE UUID: 1006dbfb-48e3-4327-a448-4c861d775d90
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Date deposited: 05 Apr 2024 16:45
Last modified: 10 Apr 2024 02:14
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Contributors
Author:
Lei Li
Author:
Wangbin Ding
Author:
Liqin Huang
Author:
Xiahai Zhuang
Author:
Vicente Grau
Corporate Author: et al.
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