Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

• URL: http://arxiv.org/abs/2107.03887v1
• Date: Thu, 8 Jul 2021 15:14:00 GMT
• Title: Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
• Authors: Shuo Wang, Chen Qin, Nicolo Savioli, Chen Chen, Declan O'Regan, Stuart Cook, Yike Guo, Daniel Rueckert and Wenjia Bai
• Abstract summary: We propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches.
• Score: 18.887520377396925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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