Abstract #0070

High-Frequency Subband Compressed Sensing with ARC Parallel Imaging

Kyunghyun Sung¹, Anderson N. Nnewihe¹^,2, Bruce L. Daniel¹, Brian A. Hargreaves¹

¹Radiology, Stanford University, Stanford, CA, USA; ²Bioengineering, Stanford University, Stanford, CA, USA

Compressed sensing (CS) is a technique that allows accurate reconstruction of images from a reduced set of acquired data. Here, we present a new method, which efficiently combines CS and parallel imaging (PI) by separating k-space sampling and reconstruction for high- and low-frequency k-space data. This maximally utilizes the wavelet-domain sparsity and avoids possible CS failure in low frequency region. This work has been demonstrated for high-resolution 3D breast imaging and the reconstructed image successfully recovered low-frequency content and fine structures with a net acceleration of 10.8.