August 14, 2024

Denoising 4D-Flow using Self-Supervised Deep Learning and its effect on test-rest reproducibility

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Abstract

Through a self-supervised deep learning denoising algorithm, more precise cerebrovascular measurements of flow, maximum fluid velocity, and vessel cross-sectional area were obtained from undersampled neurovascular 4D-Flow MRI data. This algorithm was applied to back-to-back scans to illustrate its effectiveness in reducing measurement variance.

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Cite This Study

Jolicoeur et al. (Wed,) studied this question.

synapsesocial.com/papers/68e5c61db6db64358755c7ef https://doi.org/https://doi.org/10.58530/2023/0698

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3Denoising Vastly Undersampled Radial Portal Venous 4D Flow Data via Deep Learning2024
4FlowMRI-Net: A generalizable self-supervised physics-driven 4D Flow MRI reconstruction network for aortic and cerebrovascular applications2025
5Spatiotemporal 4D-UNet for Physics Consistent Super-Resolution and Denoising of 4DFlow-MRI2025

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