Dr. Baran Gözcü
Computer-assisted Applications in Medicine (CAiM) Group, Computer Vision Lab, ETH Zurich
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. We propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy. Our learning method has access to a representative set of training signals and searches for a sampling pattern that performs well on average for the signals in this set. We present novel greedy mask selection algorithms and show them to be effective for a variety of reconstruction rules, anatomies, performance metrics, and scan settings such as static/dynamic and parallel imaging. Moreover, we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.