High-resolution dynamic 31P-MRSI using a low-rank tensor model

PurposeTo develop a rapid P-31-MRSI method with high spatiospectral resolution using low-rank tensor-based data acquisition and image reconstruction. MethodsThe multidimensional image function of P-31-MRSI is represented by a low-rank tensor to capture the spatial-spectral-temporal correlations of data. A hybrid data acquisition scheme is used for sparse sampling, which consists of a set of training data with limited k-space coverage to capture the subspace structure of the image function, and a set of sparsely sampled imaging data for high-resolution image reconstruction. An explicit subspace pursuit approach is used for image reconstruction, which estimates the bases of the subspace from the training data and then reconstructs a high-resolution image function from the imaging data. ResultsWe have validated the feasibility of the proposed method using phantom and in vivo studies on a 3T whole-body scanner and a 9.4T preclinical scanner. The proposed method produced high-resolution static P-31-MRSI images (i.e., 6.9 x 6.9 x 10 mm(3) nominal resolution in a 15-min acquisition at 3T) and high-resolution, high-frame-rate dynamic P-31-MRSI images (i.e., 1.5 x 1.5 x 1.6 mm(3) nominal resolution, 30 s/frame at 9.4T). ConclusionsDynamic spatiospectral variations of P-31-MRSI signals can be efficiently represented by a low-rank tensor. Exploiting this mathematical structure for data acquisition and image reconstruction can lead to fast P-31-MRSI with high resolution, frame-rate, and SNR. Magn Reson Med 78:419-428, 2017. (c) 2017 International Society for Magnetic Resonance in Medicine