Journal: Magnetic Resonance in Medicine

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

doi: 10.1002/mrm.27728

Figure Lengend Snippet: Intermediate solutions from the MAGO method implementation on a RADIcAL case from the Ulm site (Siemens Healthcare, Erlangen, Germany, Skyra, 3T, 12 echoes, TE 1 = 1.1 ms, Δ TE ≈ 1 ms, 3º flip angle). One parameter set ρ W , ρ F , R 2 ∗ will be obtained for each of the 2 runs of the optimization algorithm in each voxel, with 2 different sets of initial conditions ρ W , ρ F , R 2 ∗ 1 = 1000 , 0 , 50 and ρ W , ρ F , R 2 ∗ 2 = 0 , 1000 , 50 (water and fat amounts are in arbitrary units and R 2 ∗ is measured in s −1 ). The first set of initial conditions combines to PDFF = 0% and will lead to the parameter set 1 maps (A‐D); this parameter set PDFF map (C) is similar to magnitude‐based PDFF maps previously reported in the literature, where liver PDFF values are reported in the expected range, but subcutaneous and visceral PDFF values are aliased to values below 50%. The PDFF map of the parameter set 2 maps (E‐H) has subcutaneous and visceral PDFF values in the expected range, but liver PDFF is infeasibly high. The solution water‐only, fat‐only, PDFF and T 2 ∗ (1/ R 2 ∗ ) maps (I‐L) are constructed by choosing the parameter set ρ W , ρ F , R 2 ∗ with lower RSS at each pixel. Residual values for the labeled pixel (marked ∗ ) were R 1 = 51 (J) and R 2 = 510 (K) in this case. This enables robust water–fat separation and quantification of PDFF within the entire dynamic range (0‐100%). As may be noted, 2 spatially distant pixels with similar PDFF values could have substantially different RSS (see for example the subcutaneous fat in D); it is the relative difference between the RSS of the 2 parameter sets at each voxel that is evaluated. MAGO, MAGnitude‐Only; RADIcAL, non‐invasive rapid assessment of chronic liver disease using Magnetic Resonance Imaging with LiverMultiScan; T, tesla

Article Snippet: PDFF maps were generated using MAGO applied to just the magnitude images and discarding phase information, and they were also generated using an implementation of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) method , LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK), described in Hutton et al. LMS IDEAL is a complex‐based, confounder‐corrected method that includes a region growing algorithm from Yu et al. for field map estimation and a final magnitude‐based estimation step to mitigate phase errors., , LMS IDEAL also includes a correction for bipolar gradients from Peterson and Månsson.

Techniques: Construct, Labeling, Magnetic Resonance Imaging

Journal: Magnetic Resonance in Medicine

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

doi: 10.1002/mrm.27728

Figure Lengend Snippet: UK Biobank (Siemens Healthcare, Erlangen, Germany, Aera, 1.5T) results of reported segmentation median PDFF measures from the MAGO method against the reference state‐of‐the‐art hybrid method LMS IDEAL (Perspectum Diagnostics Ltd, Oxford, UK). PDFF maps from an example where LMS IDEAL rendered fat–water swap artefacts (highlighted areas) are shown (A). The contour of automatic liver segmentation masks with excluded vessels are overlapped on the PDFF images (A, B) and the difference image (C). Pixel‐wise agreement is observed in the difference image, notably within the liver mask; field map smoothness assumptions of LMS IDEAL cause apparent blurring of the LMS IDEAL PDFF map and salient borders in the difference image (C). Bland‐Altman analysis comparing all N = 178 median PDFF measures from the 2 methods is included, with small bias and confidence intervals within clinical agreement (−0.02% ± 0.13% PDFF) (D). IDEAL, iterative decomposition of water and fat with echo asymmetry and least squares estimation; LMS, LiverMultiScan

Article Snippet: PDFF maps were generated using MAGO applied to just the magnitude images and discarding phase information, and they were also generated using an implementation of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) method , LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK), described in Hutton et al. LMS IDEAL is a complex‐based, confounder‐corrected method that includes a region growing algorithm from Yu et al. for field map estimation and a final magnitude‐based estimation step to mitigate phase errors., , LMS IDEAL also includes a correction for bipolar gradients from Peterson and Månsson.

Techniques: