Our MetricsUnderstanding PDFFPerspectum Diagnostics provides a comprehensive assessment of liver health based on a range of MRI-derived biomarkers, using our flagship product LiverMultiScanTM. We use MRI-PDFF to provide a metric of hepatic fat.

What is MRI-PDFF?

PDFF is Proton Density Fat Fraction and is a measure of the proportion of a tissue which is composed of fat. Magnetic resonance imaging (MRI)-PDFF exploits the difference in resonance frequencies of protons in water and fat to provide estimates of tissue fat fraction.

This works by separating the signals from water and fat, and then calculating the percentage of the combined signal that comes from fat:

What does MRI-PDFF show?

MRI-PDFF has been shown to be closely correlated with hepatic steatosis grades from histology1

The liver acts as a regulator of fat synthesis and clearance in the body. It naturally stores small quantities of fat which act as a fuel reserve. Hepatic steatosis is characterized by excessive deposition of fat in the liver (>5% by volume). This occurs when the production of fat exceeds the rate of oxidation and is often caused by dietary fat excess, as well as genetic conditions such as hypercholesterolaemia.

MRI-PDFF has been well validated and has emerged as a useful endpoint in non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) trials. Hepatic steatosis is the primary identifier of NAFLD, and so MRI-PDFF provides a non-invasive method of patient identification and efficacy signalling (particularly for metabolic compounds).

MRI-PDFF can be delivered using most MRI scanners and so it has demonstrated utility in multi-center research studies and in clinical practice.

FAT FRACTION (%) = FATWATER + FAT

Figure 1. IDEAL Model for fat quantification

Perspectum’s Method: LMS IDEAL Traditional MRI-PDFF has several confounds which can interfere with accurate measurement of fat. In order to overcome these issues, a technique called IDEAL was developed by the University of Wisconsin2.

This technique is only routinely available on GE scanners, but Perspectum has licensed the method and we have developed LMS IDEAL, which we can deliver through LiverMultiScan across all leading MRI manufacturers and field strengths.

A meta-analysis on 425 subjects on manufacturer packages and protocols for traditional MRI-PDFF assessment showed a coefficient of variance of +/- 5.5% between different hardware and software implementations of MRI-PDFF.

In performance testing using similar methods, LMS IDEAL has a +/- 1.8% coefficient of variance different hardware, showing superior repeatability and reproducibility to traditional quantification.

Key publicationsWilman, H.R., et al. (2017). Characterisation of liver fat in the UK Biobank cohort. PLOS One, 12(2):e0172921Reeder, S., McKenzie, C. & Penada, A., 2007. Water-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging, Volume 25, pp. 644-652.Wilman, H.R., et al. Repeatability and reproducibility of multiparametric magnetic resonance imaging of the liver. (2018) Journal of Hepatology , Volume 68 , S562Hutton, C. et al. (2018) Validation of a standardized MRI method for liver fat and T2* quantification. PLOS One,13(9), p.e0204175.

References1) Noureddin, et al., (2013). Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials. Hepatology, 58(6), 1930-1940. 2) Reeder, et al., (2007) Water-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging, Volume 25, pp. 644-652.3) Yokoo, et al., (2017). Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis 286, 2.

Perspectum’s LMS IDEAL

Reproducibility coefficient:Between Scanner: +/- 1.8%

Repeatability coefficient:Within Scanner: +/- 1.9%

Meta Analysis (QIBA supported)2:

Reproducibility coefficient:Between Scanner: +/- 5.5%

Repeatability coefficient:Within Scanner: +/- 4.7%

Figure 2. LMS IDEAL image with a PDFF of 21%0 10 20 30 40 50

Figure 3. Reproducibility and repeatability meta analysis from QIBA, and Perspectum’s LMS IDEAL

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