A (multi) GPU iterative reconstruction algorithm based on Hessian penalty term for sparse MRI

A recent trend in the Magnetic Resonance Imaging (MRI) research field is to design and adopt machines that are able to acquire undersampled clinical data, reducing the time for which the patient is lying in the body scanner. Unfortunately, the missing information in these undersampled acquired datasets leads to artefacts in the reconstructed image; therefore, computationally expensive image reconstruction techniques are required. In this paper, we present an iterative regularisation strategy with a second-order derivative penalty term for the reconstruction of undersampled image datasets. Moreover, we compare this approach with other constrained minimisation methods, resulting in improved accuracy. Finally, an implementation on a massively parallel architecture environment, a multi Graphics Processing Unit (GPU) system, of the proposed iterative algorithm is presented. The resulting performance gives clinically-feasible reconstruction run times, speed-up and improvements in terms of reconstruction accuracy of the undersampled MRI images.