Rapid magnetic resonance imaging based on one dimensional under-sampling

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.