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Joint motion correction and super resolution for cardiac segmentation via latent optimisation

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Title: Joint motion correction and super resolution for cardiac segmentation via latent optimisation
Authors: Wang, S
Qin, C
Savioli, N
Chen, C
O'Regan, D
Cook, S
Guo, Y
Rueckert, D
Bai, W
Item Type: Conference Paper
Abstract: In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration and respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in clinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility.
Issue Date: 1-Oct-2021
Date of Acceptance: 1-Jul-2021
URI: http://hdl.handle.net/10044/1/90279
DOI: 10.1007/978-3-030-87199-4_2
Publisher: Springer
Start Page: 14
End Page: 24
Volume: 12903
Copyright Statement: © 2021 Springer Nature Switzerland AG. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-87199-4_2
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
Funder's Grant Number: RDC04
Conference Name: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Engineering, Biomedical
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Motion correction
Cardiac MR
Artificial Intelligence & Image Processing
Notes: The paper is early accepted to MICCAI 2021. The codes are available at https://github.com/shuowang26/SRHeart
Publication Status: Published
Start Date: 2021-09-27
Finish Date: 2021-10-01
Conference Place: Strasbourg, France
Online Publication Date: 2021-09-21
Appears in Collections:Computing
Institute of Clinical Sciences
Faculty of Medicine
Department of Brain Sciences
Faculty of Engineering