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Abstract #2257

Image Reconstruction from 3D Non-Cartesian Data Employing a Combined Conjugate Gradient and Denoising Algorithm

Gregory R. Lee1, 2, Jeffrey L. Sunshine1, 2, Mark A. Griswold1, 2

1Radiology, Case Western Reserve University, Cleveland, OH, United States; 2University Hospitals Case Medical Center, Cleveland, OH, United States

Non-Cartesian 3D acquisitions, when combined with parallel imaging and compressed sensing, have great potential for accelerating MR image acquisition. However, compressed sensing reconstructions of large 3D datasets remains computationally challenging. In the present work, a simple algorithm that alternates between iterations of a conjugate gradient SENSE algorithm and a recently proposed transform-domain denoising operation is proposed. The proposed technique does not require the tuning of any regularization parameters and requires only a background noise standard deviation as input to the denoising routine. High quality reconstructions are demonstrated for contrast-enhanced MRA images undersampled by a factor of 40-150.