A bayesian method for accelerated magnetic resonance elastography of the liver

PurposeMagnetic resonance elastography is a noninvasive tool for quantifying soft tissue stiffness. Magnetic resonance elastography has been adopted as a clinical method for staging liver fibrosis. However, the application of liver magnetic resonance elastography requires multiple lengthy breath-holds. We propose a new data acquisition and processing method to reduce magnetic resonance elastography scan time. MethodsA Bayesian image reconstruction method that uses transform sparsity and magnitude consistency across different phase offsets to recover images from highly undersampled data is proposed. The method is validated using retrospectively down-sampled phantom data and prospectively down-sampled in vivo data (N=86). ResultsThe proposed technique allows accurate quantification of mean liver stiffness up to an acceleration factor of R=6, enabling acquisition of a slice in 4.3 s. Bland-Altman analysis indicates that the proposed technique (R=6) has a bias of -0.04 kPa and limits of agreement of -0.36 to+0.28 kPa when compared with traditional generalized autocalibrating partial parallel acquisition reconstruction (R=1.4). ConclusionBy exploiting transform sparsity and magnitude consistency, accurate quantification of mean stiffness in the liver can be obtained at an acceleration rate of up to R=6. This potentially enables the collection of three to four liver slices, as per clinical protocol, within a single breath-hold. Magn Reson Med 80:1178-1188, 2018. (c) 2018 International Society for Magnetic Resonance in Medicine.