Virtual high-resolution MR angiography from non-angiographic multi-contrast MRIs: synthetic vascular model populations for in-silico trials

• Published in: Medical image analysis Vol. 87; p. 102814
• Main Authors: Xia, Yan, Ravikumar, Nishant, Lassila, Toni, Frangi, Alejandro F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2023
• Subjects: Brain - blood supply; Brain - diagnostic imaging; Conditional generative adversarial net; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Angiography - methods; Magnetic Resonance Imaging - methods; Medical image synthesis; MR angiography; Multi-contrast MRI; Medical image synthesis; Conditional generative adversarial net; Multi-contrast MRI; MR angiography
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2023.102814

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. •An end-to-end generative adversarial network that can synthesise anatomically plausible, high-resolution 3D MRA images using commonly acquired multi-contrast MR images.•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.•The proposed approach can generalise to unseen data acquired using in different imaging centres with different scanners.•The results show the potential for use of the proposed approach to generating digital twin cohorts of cerebrovascular anatomy at scale.