A Novel Compressed Sensing-Based Approach for Fast MRI Reconstruction from Highly Under-Sampled K-Space Data

Magnetic Resonance (MR) imaging is a multiparametric imaging technique allowing the diagnosis of a wide spectrum of cardiovascular diseases. Unfortunately, MR acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for reducing MR scan time, increasing image reconstruction time. In this study we formulated a novel CS-based approach to speed up reconstruction procedure. A fidelity term that constrains the solution to be similar to the acquired samples was embedded in a nonconvex weighted total variation-based approach starting from highly subsampled k-space data. This approach was tested for the reconstruction of cardiac images in 10 delayed contrast enhanced MR (DCE-MR) acquisitions, using different k-space masks. Fully sampled MR images and the reconstructed images were compared by means of peak- and signal-to-noise ratio (PSNR and SNR) metrics. Compared to other k-space filling trajectories, radial mask allowed the reconstruction of images of comparable quality (PSNR in [30 40]) but using less information. Overall, in all the test images we obtained a good reconstruction with similar SNR of the corresponding fully sampled images but using less than 20% of the original samples.