Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation. The clinical application of MRI may be limited by long data acquisition times; therefore, MR image reconstruction from highly undersampled k-space data has been an active area of research. Many works exploit rank deficiency in a Hankel data matrix to recover unobserved k-space samples; the resulting problem is non-convex, so the choice of numerical algorithm can significantly affect performance, computation, and memory. We present a simple, scalable approach called Convolutional Framework (CF). We demonstrate the feasibility and versatility of CF using measured data from 2D, 3D, and dynamic applications.
@article{arxiv.2002.03225,
title = {Convolutional Framework for Accelerated Magnetic Resonance Imaging},
author = {Shen Zhao and Lee C. Potter and Kiryung Lee and Rizwan Ahmad},
journal= {arXiv preprint arXiv:2002.03225},
year = {2020}
}
Comments
IEEE ISBI 2020, International Symposium on Biomedical Imaging