Xia, Y, Ravikumar, N, Lassila, T orcid.org/0000-0001-8947-1447 et al. (1 more author) (2023) Generating virtual patients of high-resolution MR angiography from non-angiographic multi-contrast MRIs for In-silico trials. Medical Image Analysis, 87. 102814. ISSN 1361-8415
Abstract
Despite success on multi-contrast MR image synthesis, generating specific modalities remains challenging. Those include Magnetic Resonance Angiography (MRA) that highlights details of vascular anatomy using specialised imaging sequences for emphasising inflow effect. This work proposes an end-to-end generative adversarial network that can synthesise anatomically plausible, high-resolution 3D MRA images using commonly acquired multi-contrast MR images (e.g. T1/T2/PD-weighted MR images) for the same subject whilst preserving the continuity of vascular anatomy. A reliable technique for MRA synthesis would unleash the research potential of very few population databases with imaging modalities (such as MRA) that enable quantitative characterisation of whole-brain vasculature. Our work is motivated by the need to generate digital twins and virtual patients of cerebrovascular anatomy for in-silico studies and/or in-silico trials. We propose a dedicated generator and discriminator that leverage the shared and complementary features of multi-source images. We design a composite loss function for emphasising vascular properties by minimising the statistical difference between the feature representations of the target images and the synthesised outputs in both 3D volumetric and 2D projection domains. Experimental results show that the proposed method can synthesise high-quality MRA images and outperform the state-of-the-art generative models both qualitatively and quantitatively. The importance assessment reveals that T2 and PD-weighted images are better predictors of MRA images than T1; and PD-weighted images contribute to better visibility of small vessel branches towards the peripheral regions. In addition, the proposed approach can generalise to unseen data acquired at different imaging centres with different scanners, whilst synthesising MRAs and vascular geometries that maintain vessel continuity. The results show the potential for use of the proposed approach to generating digital twin cohorts of cerebrovascular anatomy at scale from structural MR images typically acquired in population imaging initiatives.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Conditional generative adversarial net; Medical image synthesis; MR angiography; Multi-contrast MRI |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering CiET1819\19 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 May 2023 10:44 |
Last Modified: | 04 Jun 2024 13:46 |
Published Version: | http://dx.doi.org/10.1016/j.media.2023.102814 |
Status: | Published |
Publisher: | Elsevier BV |
Identification Number: | 10.1016/j.media.2023.102814 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199255 |