Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping.

Area of Science:

• Medical Imaging
• Biophysics
• Quantitative Susceptibility Mapping

Background:

• Quantitative Susceptibility Mapping (QSM) is crucial for assessing liver iron concentration (LIC).
• Current QSM methods face challenges with repeatability, reproducibility, and bias across different acquisition parameters.
• Improving QSM accuracy is vital for reliable clinical diagnosis and monitoring.

Purpose of the Study:

• To develop an optimized regularized reconstruction algorithm for abdominal QSM.
• The goal is to enhance repeatability and reproducibility across acquisition parameters and reduce bias in liver QSM.
• To improve the accuracy of liver iron concentration (LIC) quantification.

Main Methods:

• A data-adaptive method was formulated as a constrained reconstruction problem, incorporating data reliability and anatomical priors from chemical shift-encoded imaging.
• The method was evaluated for bias, repeatability, and reproducibility in patients with varying LIC.
• Comparisons were made against a standard QSM approach using two multi-echo spoiled gradient-recalled echo protocols at 3T, with analysis using linear regression and Bland-Altman plots.

Main Results:

• The data-adaptive method yielded higher subjective quality susceptibility maps with fewer shading artifacts.
• It demonstrated higher linear correlation with both $$ - and $$ -based LIC measurements compared to the standard method.
• The data-adaptive method showed significantly better test-retest repeatability and reproducibility across different acquisition protocols.

Conclusions:

• The proposed data-adaptive QSM algorithm enhances the quantification of liver iron concentration (LIC).
• It offers improved repeatability and reproducibility across various 3T MRI acquisition parameters.
• This advancement holds potential for more reliable clinical assessments of liver conditions.