Samir D. Sharma1, Krishna S. Nayak1
1Electrical Engineering, University of Southern California, Los Angeles, CA, USA
Compressed sensing (CS) MRI has been shown to provide satisfactory image reconstruction from fewer k-space samples than in traditional methods. CS assumes transform sparsity of the image. However, the use of this assumption may introduce image artifacts that could lead to misdiagnoses in clinical applications. In this work, we exploit the benefits of CS by only imposing the sparsity constraint outside of a region of primary clinical interest (ROI), where small errors are tolerable. With this approach, we are able to increase the ROI reconstruction quality without sacrificing the acceleration gain.