1 research outputs found
Convolutional Framework for Accelerated Magnetic Resonance Imaging
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.Comment: IEEE ISBI 2020, International Symposium on Biomedical Imagin