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Abstract #2436

High performance GPU enabled GRAPPA reconstruction using CUDA

Omair Inam1, Hamza Akram1, Zoia Laraib1, Mahmood Qureshi1, and Hammad Omer1

1Electrical Engineering, COMSATS University Islamabad, Islamabad, Pakistan

GRAPPA is a parallel MRI technique that enables accelerated data acquisition using multi-channel receiver coils. However, processing a large data limits the performance of GRAPPA in terms of reconstruction time. This work presents a new GPU-enabled-GRAPPA reconstruction method using optimized CUDA kernels, where multiple threads simultaneously communicate and cooperate to perform: (i) parallel fittings of GRAPPA kernel on auto-calibration signals; (ii) parallel estimations of reconstruction coefficients; (iii) parallel interpolations in under-sampled k-space. In-vivo results of 8-channel, 1.5T human head dataset show that the proposed method speeds up the GRAPPA reconstruction time up to 15x without compromising the image quality.

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