This presentation will describe MRI-based techniques for quantification of fat and iron deposition in the liver. Specifically, this presentation will cover: 1) the relevant MR contrast mechanisms related to the presence of fat and iron, 2) the types of pulse sequences used to probe these contrast mechanisms, 3) the main challenges for quantification of fat and iron, 4) the technical solutions to these challenges, and 5) the state of the art of development and validation of MRI-based techniques for quantification of fat and iron deposition in the liver.
Contrast mechanisms: Fat quantification methods are typically based on the chemical shift effect, ie: the different resonance frequency of protons within fat molecules relative to protons in water molecules (1, 2).
Types of pulse sequences: The chemical shift effect can be encoded in a variety of pulse sequences (including spin echo (3), gradient echo (4), and steady-state free-precession (5)) by acquiring several images with different echo times (or echo time offsets), leading to different relative phases between water and fat signals. However, fat quantification methods typically rely on spoiled gradient echo acquisitions (6), due to the ability of these sequences to provide fast imaging while avoiding T1 and T2 weighting (see below).
Parametric mapping: Chemical shift encoded acquisitions can be postprocessed to obtain separate fat-only and water-only images (which will generally include various contrast effects: T1, T2*, etc). From these images, a signal fat fraction (FF) map can be calculated pixel-wise as $$FF=\frac{fat}{water + fat}$$Note that generally this FF map will depend on the T1 of water and fat, which are generally different, as well as other imaging and contrast parameters (confounding factors). However, upon correction for all relevant confounding factors, we can obtain fat-only and water-only images that contain only proton-density weighting. Based on these images, we can calculate the proton-density fat fraction (PDFF), as (7, 8) $$PDFF=\frac{fat}{water + fat}$$
Confounding factors: General imaging challenges such as motion and parallel imaging artifacts can complicate fat quantification in the liver. In addition, specific confounders for fat quantification include T1 and T2* relaxation, B0 magnetic field inhomogeneities, the spectral complexity of the fat signal, and noise-related bias, among others (6, 7, 9-11).
Technical solutions: Fat quantification methods address the confounders described above through a combination of acquisition-based and post-processing based methods. For instance, T1 bias is typically addressed with acquisition-based methods, by acquiring spoiled gradient echo images with low flip angles to avoid T1 contrast (10). T2* relaxation is typically addressed by including T2* in the post-processing (parametric mapping) signal model, ie: simultaneously mapping T2* (or R2*=1/T2*) and PDFF (6, 7).
State-of-the-art: Liver fat quantification methods have been validated in multiple clinical studies in various patient populations, different vendors, field strengths, and platforms (12). In addition, advanced methods have been recently developed that enable free-breathing scanning with high image quality (13), as well as more sophisticated signal models that may enable characterization (in addition to quantification) of fat (14).
Contrast mechanisms: Unlike fat deposition, which can be observed directly in 1H MRI, iron deposition is typically observed in MRI through its effect on water signals. Indeed, iron deposition affects a variety of MRI contrast mechanisms, including magnetic susceptibility, T2, T2, and T2*. In practice, most currently available liver iron quantification methods are based either on T2 (or R2=1/T2) relaxometry (15) or T2* (or R2*=1/T2*) relaxometry (16).
Types of pulse sequences: Liver iron quantification is commonly performed using multi-echo spin echo (for R2 mapping), or multi-echo gradient echo (for R2* mapping) acquisitions. Parametric mapping: R2 or R2* relaxometry is performed by non-linear fitting of the multi-echo acquisitions described above. Based upon appropriate calibration curves, liver iron concentration (LIC, eg: in mg Fe/g dry tissue) can be calculated from R2 or R2* measurements in the liver (15, 16).
Confounding factors: Confounders to iron quantification include the presence of motion artifacts, the presence of multiple signal components (ie: non-monoexponential signal decay due to the presence of fat or other effects), and noise-related bias, among others (17-19).
Technical solutions: Iron quantification methods address the confounders described above through a combination of acquisition-based and post-processing based methods. Acquisition based methods include the use of short echo times to avoid noise-related bias at high iron concentrations (16, 20). Post-processing based methods include the use of multi-component signal models instead of simple mono-exponential models during parametric mapping (19).
State-of-the-art: Various R2- and R2*-based techniques have been validated and calibrated using biopsy as a reference standard (15, 16). In addition, recent studies have evaluated the reproducibility of iron quantification across different patient populations, MRI vendors, and platforms (21-23). Iron quantification is typically performed at 1.5T due to the rapid signal decay observed at 3.0T in the presence of high liver iron concentrations, which limits the dynamic range of reliable iron quantification at 3.0T. However, recent developments including the use of ultra-short echo times may enable iron quantification at 3.0T with a wide dynamic range (20).
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