Rapid magnetic resonance imaging based on one dimensional under-sampling

Peiyao Sun; Qiyang Gu; Ruitong Wang

doi:10.1117/12.3014564

9 January 2024 Rapid magnetic resonance imaging based on one dimensional under-sampling

Peiyao Sun, Qiyang Gu, Ruitong Wang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129691J (2024) https://doi.org/10.1117/12.3014564
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

This paper introduces a novel approach to accelerate Magnetic Resonance Imaging (MRI) using 1-dimensional undersampling and compressed sensing. By strategically applying under-sampling to rows through a Gaussian distribution, the proposed method aims to reduce the number of samples required for image reconstruction while maintaining image quality. The reconstruction process involves denoising with a Projection Over Convex Sets (POCS) algorithm, optimizing the threshold parameter lambda (λ) for effective denoising and convergence. Simulation results showcase the method’s effectiveness. Reconstructed images at varying under-sampling rates illustrate the gradual reduction of artifacts with increased mid-frequency sampling. The study also explores different lambda settings during reconstruction, highlighting the balance between denoising and convergence. While this approach shows promise for accelerating MRI and other imaging applications, challenges include evaluating alternative "mask" matrices and exploring under-sampling patterns beyond Gaussian distribution. The paper concludes by emphasizing compressed sensing’s potential to enhance applications constrained by scan time, fostering optimism for broader adoption.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peiyao Sun, Qiyang Gu, and Ruitong Wang "Rapid magnetic resonance imaging based on one dimensional under-sampling", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129691J (9 January 2024); https://doi.org/10.1117/12.3014564

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