A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
The long scan times of Magnetic Resonance Imaging (MRI) create a bottleneck in patient care and acquisitions can be accelerated by under-sampling in k-space (i.e., the Fourier domain). In this thesis, we focus on the optimization of the sub-sampling pattern with a data-driven framework. Since the reconstruction quality of the models are shown to be strongly dependent on the sub-sampling pattern, we combine the two problems. For a provided sparsity constraint, our method optimizes thedoi:10.1007/978-3-030-20351-1_61 fatcat:vy6ojeq3d5bwjnct4s3bertk3a