Liver: Fat Quantification
S. Sendhil Velan1
1Laboratory of Molecular Imaging, Institute of Bioengineering and Bioimaging, A*STAR, Singapore, Singapore

Synopsis

This presentation will cover the MRI/MRS-based techniques for the quantification of liver fat. Specifically, this presentation will include relevant MRI/MRS techniques, types of pulse sequences utilized in a clinical setting, challenges, and finally, the state of the art of development and validation of MRI-based approaches for quantification of liver fat.

Introduction

The liver coordinates the whole body's metabolic flexibility, characterized by the ability to adapt dynamically in response to fluctuations in energy needs and supplies. Liver diseases, including alcohol related fatty liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), liver cirrhosis and hepatocellular carcinoma (HCC), account for over 3 million deaths per year worldwide. Hepatic steatosis is a condition of the liver characterized by the accumulation of lipid within hepatocytes. The minimum criterion for the histological diagnosis of NAFLD is the existence of > 5% hepatocytes with steatosis or "steatotic hepatocytes". Liver biopsy is currently considered the gold standard to determine increased liver fat content. However, liver biopsy suffers from diagnostic limitations and is risky, making it less than ideal for screening and monitoring. Magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) based methods provide quantitative information on liver fat.

Fat Quantification by MRS

Fat quantification methods are typically based on the Point REsolved Spectroscopy (PRESS) and STimulated Echo Acquisition Mode (STEAM) sequences [1,2]. These methods are widely accepted for the quantification of fat [3-4]. Multivoxel MRS spectroscopic methods (MRSI) use 2D or 3D phase encoding to extend single-voxel MRS to characterize spatial variations in fat content [5-7]. MRSI methods are more challenging in the liver due to large spatial coverage, shimming, and requirements for breath-holding time. From the MRS data, proton density fat fraction (PDFF) can be estimated by computing the ratio between MR observable fat protons and all MR observable fat and water protons [8,9]. To accurately measure PDFF, acquisition should be performed with a long repetition time (TR) to avoid T1 effects. The acquired signal should also be corrected for fat and water T2 losses [10, 11]. Relaxation correction using T2 of water and fat can be performed in a single acquisition for the calculation of PDFF [12].

Fat Quantification by MRI

The chemical shifts can be encoded in a variety of pulse sequences, including spin echoes, gradient echoes, steady state free precision by acquiring several images with different echo times leading to different phases between water and fat signals. However, fat quantification methods typically rely on spoiled gradient echo acquisition due to the ability of these sequences to provide fast imaging while avoiding T1 and T2* effects.
Parametric mapping: Chemical shift encoded acquisitions can be post-processed to obtain separate fat-only and water-only images which can be corrected for various contrast effects (T1, T2*, etc) followed by a pixel wise PDFF map calculation. Fat quantification methods address the confounders described through a combination of acquisition based and post processing methods. For instance T1 bias is avoided by acquiring spoiled gradient echo images with low flip angles and long TR. T2* relaxation is typically addressed by including T2* correction during post-processing to achieve quantitative PDFF [13,14]. The other confounders that should also be considered include spectral complexity taking into account the frequency and amplitudes of various fat peaks. Noise bias can also impact the quantification when there is low fat signal. Eddy currents due to rapidly switching gradients can also lead to phase shifts resulting in artifacts [15].

State of the art

Liver fat quantification by MRS and MRI methods have been validated in multiple clinical studies in various patient populations, different vendors, field strengths, and platforms (16-18). In addition, recently advanced methods have been developed that enable imaging of liver with free-breathing [19], as well as more sophisticated signal models that may enable characterization of fatty acid characterization including saturated, unsaturated, monounsaturation and polyunsaturation in addition to quantify of fat [20].

Acknowledgements

The author acknowledges funding support from Institute of Bioengineering and Bioimaging (IBB), A*STAR.

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Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)