Learning-Based Compressive MRI
This work addresses the challenge of improving MRI efficiency and quality for medical imaging applications, representing an incremental advance by integrating pattern design with reconstruction and anatomy.
The paper tackles the problem of designing MRI subsampling patterns by proposing a learning-based framework that optimizes patterns for specific reconstruction rules and anatomies, achieving effective performance across various metrics and settings.
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. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm 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 a novel parameter-free greedy mask selection method, and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.