Non-Invasive Testing for Liver Fibrosis: Multiparametric Liver Quantification
Sudhakar K. Venkatesh1
1Mayo Clinic, United States

Synopsis

Keywords: Body: Liver, Contrast mechanisms: Elastography, Image acquisition: Multiparametric

MR Elastography has emerged as the leading non-invasive quantitative imaging marker of liver fibrosis and considered the reference standard. Advanced MRE technique known as “3D MRE” provides an opportunity for evaluation of new mechanical parameters including storage modulus (G’), loss modulus (G”), wave attenuation (α), damping ratio (ζ), and volumetric strain. Multiparametric evaluation including two or more of these MR parameters are promising for differentiating inflammation from fibrosis and improving diagnostic accuracy for liver fibrosis staging. Furthermore, the multiparametric MRE may aid in the diagnosis of portal hypertension and prediction of outcome in chronic liver diseases.

Liver biopsy, the current standard for diagnosing liver fibrosis is invasive and associated with risk of complications, paving the way for non-invasive tests. MR Elastography is in the forefront and currently the most accurate noninvasive imaging test for liver fibrosis. Advanced MRE technique known as “3D MRE” provides an opportunity for evaluation of new mechanical parameters including storage modulus (G’), loss modulus (G”), wave attenuation (α), damping ratio (ζ), and volumetric strain. Multiparametric evaluation including two or more of these MR parameters are promising for differentiating inflammation from fibrosis and improving diagnostic accuracy for liver fibrosis staging. Furthermore, the multiparametric MRE may aid in the diagnosis of portal hypertension and prediction of outcome in chronic liver diseases.
Multi-parametric MRI by combining MRE with other imaging techniques like T1-relaxation (which increases with fibrosis), diffusion weighted imaging (which detects changes in water movement within tissues and decreases in fibrosis). Input from other quantification parameters such as proton density fat fraction (PDFF) and liver iron content as provides valuable information. Machine learning integration and Radiomics may also boost the accuracy of the diagnosis and staging liver fibrosis.
These advances in MRI are paving the way for a future where multiparametric-MRI becomes the gold standard for evaluating liver fibrosis, offering a safer, more accurate, and less invasive approach for patients and clinicians alike.

Acknowledgements

No acknowledgement found.

References

No reference found.
Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)