This work assesses the effects of different simulated and data-driven subspaces on quantitative parameter estimation in highly accelerated subspace reconstructions. Three different methods for generating basis were applied in a locally low rank subspace reconstruction: (1) a data-driven basis derived from fully sampled low-resolution data, (2) a simulated basis from a dictionary with uniform tissue distribution, and (3) a simulated basis with white matter (WM) and gray matter (GM) oversampled. Obtaining basis vectors from a dictionary with non-uniform tissue distributions can be interpreted as obtaining basis vectors using a weighted singular value decomposition to a dictionary generated with uniform tissue spacings. Fully sampled echo planar spectroscopic imaging (EPSI) datasets of six subjects (including three brain tumor patients) were retrospectively undersampled with 9-shot and 3-shot traversals. The effect of regularization was assessed by comparing maps and qualitative images of fully sampled and retrospectively undersampled images. Under 3-shot retrospective undersampling, images reconstructed with the basis generated from a uniform distribution basis had altered signal evolutions, resulting in statistically significant WM/GM overestimations with mean differences of 10-15 ms. Applying a WM / GM oversampled basis improved accuracy but still induced bias. The data-driven basis provided the most accurate estimates. Highly accelerated subspace reconstructions cause bias in quantitative relaxation maps. Data-driven subspaces can reduce this bias.
Bai et al. (Wed,) studied this question.