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Clinical MRI Quantification

Quantitative Liver MRI, Perfected.

Clinical-grade PDFF & R2* analysis powered by the MAGO algorithm. Trusted measurement accuracy for hepatic steatosis and iron quantification.

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ROI 1ROI 2ROI 3Slice 3 / 5
PDFF
6.4%
R2*
78 s⁻¹
SD
0.8%
< 1%
PDFF agreement
vs MATLAB reference
197
Pixels per ROI
standardised area
5
Slice protocol
standardised coverage

Workflow

From DICOM to Diagnosis-Ready Data

01

Upload

Drop your DICOM folder. Series and echoes are auto-detected from the header metadata.

02

Place ROIs

Select the best slice and place 3 ROIs per slice across the liver parenchyma.

03

Analyse

Lock ROIs and run the MAGO magnitude fitting pipeline. Results appear per-slice and in aggregate.

04

Export

Save to a study, export full per-ROI CSV data, or download DICOM-format parametric maps.

Methodology

The Physics Behind the Measurement

Spectral model
Multi-peak fat model (Hamilton et al. 2011) — 6 fat peaks at known chemical shifts relative to water.
Signal fitting
3-parameter magnitude model: water amplitude W, fat amplitude F, R2* decay rate — fitted per voxel.
Field correction
B0 field strength correction applied automatically for 1.5T and 3T acquisitions.
T1 bias
T1 bias correction for flip angle effects ensures accurate fat fraction across acquisition protocols.
Motion
Rigid registration across echo times corrects for intra-acquisition motion during breath-hold imaging.
Noise floor
Noise floor bias correction applied in the magnitude domain before fitting.
SIGNAL MODEL — multi-echo magnitude
Echo Time (TE)|S(TE)|TE1TE2TE3TE4TE5TE6W onlyPDFF model fitPDFF = 6.4%R2* = 78 s⁻¹

Validation

Validated Against the Gold Standard

FibroOx measurements show < 1% absolute agreement with MATLAB pdffRecon (MAGO) across a 10-case validation cohort spanning 0–25% PDFF and 40–185 s⁻¹ R2*.

PDFF Bias
< 0.3%
R2* Bias
< 1.1 s⁻¹
Validation cases
10

Ready to quantify with confidence?

Start using FibroOx for your liver MRI research today. No installation required — runs in your browser.

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