Free breathing cardiac perfusion MRI reconstruction using a sparse and low rank model: Validation with the Physiologically Improved NCAT phantom

Abstract

We recently proposed an accelerated dynamic magnetic resonance imaging (MRI) reconstruction algorithm that exploits the underlying low rank and sparse properties of the data to achieve highly accelerated reconstructions. In this paper, we validate our algorithm in the context of dynamic free breathing cardiac Perfusion MRI on the Physiologically Improved Non Uniform Cardiac Torso Phantom, PINCAT phantom. The practical utilities of our scheme in providing significantly better reconstructions at higher accelerations in comparison to existing methods are studied. We demonstrate that our scheme do not have trade offs with accurate temporal modeling and spatial quality unlike the existing low rank based schemes. Our results also show the capability of our scheme to achieve better reconstruction qualities at high accelerations in comparison to using only the low rank or sparsity properties individually. We argue that the speed up obtained by our scheme could be capitalized in perfusion imaging to provide better spatio-temporal resolutions and volume coverage while the subject is freely breathing.