Data Adaptive Regularization for Abdominal Quantitative Susceptibility Mapping

TL;DR

This paper introduces a data-adaptive regularization method for liver QSM that enhances image quality, reduces bias, and improves reproducibility across different MRI acquisition protocols at 3T.

Contribution

The study develops a novel data-adaptive regularized reconstruction algorithm for abdominal QSM, improving repeatability and reproducibility over existing methods.

Findings

Higher correlation with R2 and R2* measurements.

Improved test-retest repeatability.

Better reproducibility across protocols.

Abstract

Purpose: To improve repeatability and reproducibility across acquisition parameters and reduce bias in quantitative susceptibility mapping (QSM) of the liver, through development of an optimized regularized reconstruction algorithm for abdominal QSM. Theory and Methods: An optimized approach to estimation of magnetic susceptibility distribution is formulated as a constrained reconstruction problem that incorporates estimates of the input data reliability and anatomical priors available from chemical shift-encoded imaging. The proposed data-adaptive method was evaluated with respect to bias, repeatability, and reproducibility in a patient population with a wide range of liver iron concentration (LIC). The proposed method was compared to the state-of-the-art approach in liver QSM for two multi-echo SGRE protocols with different acquisition parameters at 3T. Results: The data-adaptive method produced susceptibility maps with higher subjective quality due to reduced shading artifacts. For both acquisition protocols, higher linear correlation with both $R_2$ and $R_2^*$-based measurements were observed for the data-adaptive method ($r^2=0.74/0.72$ for $R_2$, $0.98/0.99$ for $R_2^*$) than the standard method ($r^2=0.62/0.67$ and $0.84/0.91$). For both protocols, the data-adaptive method enabled better test-retest repeatability (repeatability coefficients 0.14/0.14ppm for the data-adaptive method, 0.26/0.31ppm for the standard method) and reproducibility across protocols (reproducibility coefficient 0.25ppm vs 0.36ppm) than the standard method. Conclusions: The proposed data-adaptive QSM algorithm may enable quantification of liver iron concentration with improved repeatability/reproducibility across different acquisition parameters as 3T.