Accelerating 3D-T_1ρ mapping of cartilage using compressed sensing with different sparse and low rank models.

ABSTRACT:

Purpose

To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T_1ρ mapping of cartilage and to reduce total scan times without degrading the estimation of T_1ρ relaxation times.

Methods

Fully sampled 3D-T_1ρ datasets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using principal component analysis (PCA) and K-means singular value decomposition (K-SVD), explicit exponential models, low rank and low rank plus sparse models. Spatial filtering prior to T_1ρ parameter estimation was also tested. Synthetic phantom (n = 6) and in vivo human knee cartilage dataset

SUBMITTER: Zibetti MVW 

PROVIDER: S-EPMC6097944 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature