Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI

• URL: http://arxiv.org/abs/2302.12835v1
• Date: Fri, 24 Feb 2023 08:42:04 GMT
• Title: Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI
• Authors: Simone Saitta, Marcello Carioni, Subhadip Mukherjee, Carola-Bibiane Sch\"onlieb, Alberto Redaelli
• Abstract summary: Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements and a real 4D flow MRI scan. Our optimized SIREN architecture outperformed state-of-the-art techniques, producing denoised and super-resolved velocity fields from clinical data.
• Score: 1.207455285737927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 4D flow MRI is a non-invasive imaging method that can measure blood flow velocities over time. However, the velocity fields detected by this technique have limitations due to low resolution and measurement noise. Coordinate-based neural networks have been researched to improve accuracy, with SIRENs being suitable for super-resolution tasks. Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI, achieving denoising and super-resolution. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements and a real 4D flow MRI scan. Our optimized SIREN architecture outperformed state-of-the-art techniques, producing denoised and super-resolved velocity fields from clinical data. Our approach is quick to execute and straightforward to implement for novel cases, achieving 4D super-resolution.

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