Embedded quantitative MRI T1rho mapping using non-linear primal-dual proximal splitting

TL;DR

This paper introduces a novel embedded reconstruction method for T1rho mapping in quantitative MRI that directly estimates tissue parameters from undersampled data, outperforming traditional two-step approaches especially at higher acceleration factors.

Contribution

The paper presents a direct non-linear optimization-based reconstruction method for T1rho mapping that reduces unknowns and improves accuracy over existing compressed sensing techniques.

Findings

Embedded model outperforms reference methods in simulations and ex vivo measurements.

Method maintains high accuracy at higher acceleration factors.

Direct parameter map reconstruction reduces reconstruction errors.

Abstract

Quantitative MRI (qMRI) methods allow reducing the subjectivity of clinical MRI by providing numerical values on which diagnostic assessment or predictions of tissue properties can be based. However, qMRI measurements typically take more time than anatomical imaging due to requiring multiple measurements with varying contrasts for, e.g., relaxation time mapping. To reduce the scanning time, undersampled data may be combined with compressed sensing reconstruction techniques. Typical CS reconstructions first reconstruct a complex-valued set of images corresponding to the varying contrasts, followed by a non-linear signal model fit to obtain the parameter maps. We propose a direct, embedded reconstruction method for T1rho mapping. The proposed method capitalizes on a known signal model to directly reconstruct the desired parameter map using a non-linear optimization model. The proposed reconstruction method also allows directly regularizing the parameter map of interest, and greatly reduces the number of unknowns in the reconstruction. We test the proposed model using a simulated radially sampled data from a 2D phantom and 2D cartesian ex vivo measurements of a mouse kidney specimen. We compare the embedded reconstruction model to two CS reconstruction models, and in the cartesian test case also iFFT. The proposed, embedded model outperformed the reference methods on both test cases, especially with higher acceleration factors.