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Abstract #2485

Sub-second Compressed Sensing Reconstruction for Large Array Data Using GPUs

Ching-Hua Chang 1 and Jim Ji 1

1 Texas A&M University, College Station, Texas, United States

Combining compressed sensing (CS) MRI with parallel imaging can reduce the scan time and/or improve reconstruction quality. However, the iterative reconstruction algorithm required by compressive sensing is time-consuming. Several groups have reported using graphics processing units (GPUs) to accelerate CS reconstruction. However, none has been applied to CS-MRI with parallel imaging. This paper presents a method that uses an alternating direction algorithm and GPUs for CS reconstruction from parallel receive channels, which is particularly suitable for large array data. Experiments show that it takes less than a second to reconstruct a 12812816 3-D image from 8-channel data, which is more than 20 times faster than a quad-core, high-end commodity CPU.

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