Magnitude‐intrinsic water–fat ambiguity can be resolved with multipeak fat modeling and a multipoint search method

Abstract

Purpose To develop a postprocessing algorithm for multiecho chemical‐shift encoded water–fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0‐100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state‐of‐the‐art complex‐based methods, and to evaluate its robustness to artefacts in abdominal PDFF maps. Methods We introduce MAGO (MAGnitude‐Only), a magnitude‐based reconstruction that embodies multipeak liver fat spectral modeling and multipoint optimization, and which is compatible with asymmetric echo acquisitions. MAGO is assessed first for accuracy and reproducibility on publicly available phantom data. Then, MAGO is applied to N = 178 UK Biobank cases, in which its liver PDFF measures are compared using Bland‐Altman analysis with those from a version of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) algorithm, LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK). Finally, MAGO is tested on a succession of high field challenging cases for which LMS IDEAL generated artefacts in the PDFF maps. Results Phantom data showed accurate, reproducible MAGO PDFF values across manufacturers, field strengths, and acquisition protocols. Moreover, we report excellent agreement between MAGO and LMS IDEAL for 6‐echo, 1.5 tesla human acquisitions (bias = −0.02% PDFF, 95% confidence interval = ±0.13% PDFF). When tested on 12‐echo, 3 tesla cases from different manufacturers, MAGO was shown to be more robust to artefacts compared to LMS IDEAL. Conclusion MAGO resolves the water–fat ambiguity over the entire fat fraction dynamic range without compromising accuracy, therefore enabling robust PDFF estimation where phase data is inaccessible or unreliable and complex‐based and hybrid methods fail.

Comparison between magnetic resonance and ultrasound-derived indicators of hepatic steatosis in a pooled NAFLD cohort

“…LMS-IDEAL uses an advanced post-processing technique called MAGO (magnitude only reconstruction), which resolves the water-fat ambiguity over the entire fat fraction dynamic range without compromising accuracy. Therefore, robust PDFF estimations are enabled where phase data is inaccessible or unreliable, and where hybrid and complex-based methods (typically used by other MRI PDFF techniques) may fail [ 23 ]. The direct comparison between LMS-IDEAL and CAP measurements showed a strong correlation, but with a wide spread of values across all fat levels.…”

“…Anonymised MRI data were processed and analysed centrally by expertly trained image analysts using Liver MultiScan . The processing included the calculation of LMS-IDEAL PDFF maps of the liver (measured in %) using proprietary algorithms based on the multispectral IDEAL approach, which is robust to MRI-related confounds [ 23 ]. Analysis included the calculation of LMS-IDEAL measures from the median value over three manually placed regions of interest in the right lobe of the liver, avoiding image artefacts and vessels ( Fig 1 ).…”

“…Recently the IDEAL technique for MRI-based liver fat quantification has been further refined to be robust to variations in how different MRI scanners process data [ 23 ]. These developments have resulted in a technique, referred to here as LMS-IDEAL, that quantifies liver fat in a standardised way across different manufacturers and field strengths.…”

Greater ectopic fat deposition and liver fibroinflammation and lower skeletal muscle mass in people with type 2 diabetes

“…The contrasts and brightness of the slabs were adjusted automatically prior to processing the entire acquired volume. PDFF maps of the pancreas were reconstructed from the dedicated pancreas gradient‐recalled echo (GRE) 10‐echo acquisition (echo time [TE] 1 = 2.38 milliseconds, ΔTE = 2.38 milliseconds) using a magnitude‐based multipoint water‐fat separation algorithm (20).…”

Pancreas MRI segmentation into head, body, and tail enables regional quantitative analysis of heterogeneous disease

“…The median values from head, body and tail were reported after reslicing the parts segmentation onto the reconstructed proton density fat fraction (PDFF) map. A confounder-corrected magnitude-based chemical-shift encoding method [39] was used to reconstruct PDFF maps from the raw 10-echo GRE data. A spectral model from liver fat was used [40].…”