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.References
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