  Magnetic resonance imaging is capable of producing volumetric images without
ionizing radiation. Nonetheless, long acquisitions lead to prohibitively long
exams. Compressed sensing (CS) can enable faster scanning via sub-sampling with
reduced artifacts. However, CS requires significantly higher reconstruction
computation, limiting current clinical applications to 2D/3D or
limited-resolution dynamic imaging. Here we analyze the practical limitations
to T2 Shuffling, a four-dimensional CS-based acquisition, which provides sharp
3D-isotropic-resolution and multi-contrast images in a single scan. Our
improvements to the pipeline on a single machine provide a 3x overall
reconstruction speedup, which allowed us to add algorithmic changes improving
image quality. Using four machines, we achieved additional 2.1x improvement
through distributed parallelization. Our solution reduced the reconstruction
time in the hospital to 90 seconds on a 4-node cluster, enabling its use
clinically. To understand the implications of scaling this application, we
simulated running our reconstructions with a multiple scanner setup typical in
hospitals.
