High‐resolution dynamic 31P‐MRSI using a low‐rank tensor model. Issue 2 (28th May 2017) - Ma, Chao; Clifford, Bryan; Liu, Yuchi; Gu, Yuning; Lam, Fan; Yu, Xin; Liang, Zhi‐Pei
Abstract : Purpose: To develop a rapid 31 P‐MRSI method with high spatiospectral resolution using low‐rank tensor‐based data acquisition and image reconstruction. Methods: The multidimensional image function of 31 P‐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. Results: We 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 31 P‐MRSI images (i.e., 6.9 × 6.9 × 10 mm 3 nominal resolution in a 15‐min acquisition at 3T) and high‐resolution, high‐frame‐rate dynamic 31 P‐MRSI images (i.e., 1.5 × 1.5 × 1.6 mm 3 nominal resolution, 30 s/frame at 9.4T). Conclusions: Dynamic spatiospectral variations of 31 P‐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 31 P‐MRSI with high resolution, frame‐rate, and SNR. Magn Reson Med 78:419–428, 2017. © 2017 International Society for Magnetic Resonance in Medicine