Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations

• URL: http://arxiv.org/abs/2303.15065v3
• Date: Fri, 5 Jan 2024 00:48:20 GMT
• Title: Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations
• Authors: Julian McGinnis, Suprosanna Shit, Hongwei Bran Li, Vasiliki Sideri-Lampretsa, Robert Graf, Maik Dannecker, Jiazhen Pan, Nil Stolt Ans\'o, Mark M\"uhlau, Jan S. Kirschke, Daniel Rueckert, Benedikt Wiestler
• Abstract summary: Implicit Neural Representations (INR) proposed to learn two different contrasts of complementary views in a continuous spatial function. Our model provides realistic super-resolution across different pairs of contrasts in our experiments with three datasets.
• Score: 9.683341998041634
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views suffer from poor out-of-plane resolution and affect downstream volumetric image analysis that typically requires isotropic 3D scans. Combining different views of multi-contrast scans into high-resolution isotropic 3D scans is challenging due to the lack of a large training cohort, which calls for a subject-specific framework. This work proposes a novel solution to this problem leveraging Implicit Neural Representations (INR). Our proposed INR jointly learns two different contrasts of complementary views in a continuous spatial function and benefits from exchanging anatomical information between them. Trained within minutes on a single commodity GPU, our model provides realistic super-resolution across different pairs of contrasts in our experiments with three datasets. Using Mutual Information (MI) as a metric, we find that our model converges to an optimum MI amongst sequences, achieving anatomically faithful reconstruction. Code is available at: https://github.com/jqmcginnis/multi_contrast_inr/

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