Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T in the presence of fatty infiltration in skeletal muscle tissue of patients with neuromuscular diseases.

The main pathologies in the muscles of patients with neuromuscular diseases (NMD) are fatty infiltration and edema. Recently, quantitative magnetic resonance (MR) imaging for determination of the MR biomarkers proton density fat fraction (PDFF) and water T (T ) has been advanced. Biophysical effects or pathology can have different effects on MR biomarkers. Thus, for heterogeneously affected muscles, the routinely performed mean or median value analyses of MR biomarkers are questionable. Our work presents a voxel-based histogram analysis of PDFF and T images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T mapping with spectral adiabatic inversion recovery (SPAIR) for T determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T maps were coregistered. A voxel-based comparison of PDFF and T showed a decreased T with increasing PDFF. Mean T and mean T (PDFF < 10%) show good agreement, whereas standard deviation (σ) T and σ T show increasing difference with increasing values of σ. Thereby two subgroups can be observed, referring to muscles in which the exclusion of fatty voxels has a negligible influence versus muscles in which a strong dependency of the T value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T elevation, and (ii) a dependency of T on the PDFF leading to decreased T , the T distribution within a muscle might be heterogenous and the routine mean or median analysis can lead to a misinterpretation of the muscle health. It was concluded that muscle T mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T histograms is a potential alternative.