KEYWORDS: Magnetic resonance imaging, Iterative methods, Image quality, Convolution, In vivo imaging, Fourier transforms, Data modeling, Signal to noise ratio, Medical imaging, Reconstruction algorithms
Patient motion during scanning will introduce artifacts in the reconstructed image in MRI imaging. Periodically Rotated
Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) MRI is an effective technique to correct for
motion artifacts. The iterative method that combine the preconditioned conjugate gradient (PCG) algorithm with
nonuniform fast Fourier transformation (NUFFT) operations is applied to PROPELLER MRI in the paper. But the
drawback of the method is long reconstruction time. In order to make it viable in clinical situation, parallel optimization
of the iterative method on modern GPU using CUDA is proposed. The simulated data and in vivo data from
PROPELLER MRI are respectively reconstructed in order to test the method. The experimental results show that image
quality is improved compared with gridding method using the GPU based iterative method with compatible
reconstruction time.
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