Learning Two-factor Representation for Magnetic Resonance Image Super-resolution

• URL: http://arxiv.org/abs/2409.09731v1
• Date: Sun, 15 Sep 2024 13:32:24 GMT
• Title: Learning Two-factor Representation for Magnetic Resonance Image Super-resolution
• Authors: Weifeng Wei, Heng Chen, Pengxiang Su,
• Abstract summary: We propose a novel method for MR image super-resolution based on two-factor representation. Specifically, we factorize intensity signals into a linear combination of learnable basis and coefficient factors. Our method achieves state-of-the-art performance, providing superior visual fidelity and robustness.
• Score: 1.294284364022674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is a feasible solution. However, most existing methods face challenges in accurately learning a continuous volumetric representation from low-resolution image or require HR image for supervision. To solve these challenges, we propose a novel method for MR image super-resolution based on two-factor representation. Specifically, we factorize intensity signals into a linear combination of learnable basis and coefficient factors, enabling efficient continuous volumetric representation from low-resolution MR image. Besides, we introduce a coordinate-based encoding to capture structural relationships between sparse voxels, facilitating smooth completion in unobserved regions. Experiments on BraTS 2019 and MSSEG 2016 datasets demonstrate that our method achieves state-of-the-art performance, providing superior visual fidelity and robustness, particularly in large up-sampling scale MR image super-resolution.

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