Sparsity transform k‐t principal component analysis for accelerating cine three‐dimensional flow measurements

Time‐resolved three‐dimensional flow measurements are limited by long acquisition times. Among the various acceleration techniques available, k‐t methods have shown potential as they permit significant scan time reduction even with a single receive coil by exploiting spatiotemporal correlations. In this work, an extension of k‐t principal component analysis is proposed utilizing signal differences between the velocity encodings of three‐directional flow measurements to further compact the signal representation and hence improve reconstruction accuracy. The effect of sparsity transform in k‐t principal component analysis is demonstrated using simulated and measured data of the carotid bifurcation. Deploying sparsity transform for 8‐fold undersampled simulated data, velocity root‐mean‐square errors were found to decrease by 52 ± 14%, 59 ± 11%, and 16 ± 32% in the common, external, and internal carotid artery, respectively. In vivo, errors were reduced by 15 ± 17% in the common carotid artery with sparsity transform. Based on these findings, spatial resolution of three‐dimensional flow measurements was increased to 0.8 mm isotropic resolution with prospective 8‐fold undersampling and sparsity transform k‐t principal component analysis reconstruction. Volumetric data were acquired in 6 min. Pathline visualization revealed details of helical flow patterns partially hidden at lower spatial resolution. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.