Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T2 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 T₂ (T_2w ) 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_2w images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T₂ mapping with spectral adiabatic inversion recovery (SPAIR) for T_2w determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T₂ maps were coregistered. A voxel-based comparison of PDFF and T_2w showed a decreased T_2w with increasing PDFF. Mean T_2w and mean T_2w ^{without fatty voxels} (PDFF < 10%) show good agreement, whereas standard deviation (σ) T_2w and σ T_2w ^{without fatty voxels} 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_2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T_2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T_2w elevation, and (ii) a dependency of T_2w on the PDFF leading to decreased T_2w , the T_2w 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_2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T_2w histograms is a potential alternative.

Relationship between markers of disease activity and progression in skeletal muscle of GNE myopathy patients using quantitative nuclear magnetic resonance imaging and 31P nuclear magnetic resonance spectroscopy

Background

Quantitative nuclear magnetic resonance imaging (NMRI) is an objective and precise outcome measure for evaluating disease progression in neuromuscular disorders. We aimed to investigate predictive 'disease activity' NMR indices, including water T₂ and ³¹P NMR spectroscopy (NMRS), and its relation to NMR markers of 'disease progression', such as the changes in fat fraction (ΔFat%) and contractile cross-sectional area (ΔcCSA), in GNE myopathy (GNEM) patients.

Methods

NMR was performed on a 3T clinical scanner, at baseline and at a 1-year interval, in 10 GNEM patients and 29 age-matched controls. Dixon-based fat-water imaging and water T₂ mapping were acquired in legs and thighs, and in the dominant forearm. ³¹P NMRS was performed at the level of quadriceps and hamstring. Water T₂ and ³¹P NMRS indices were determined for all muscle groups and visits. Correlations were performed with 'disease progression' indices ΔFat%, ΔcCSA and the muscle fat transformation rate (R_{muscle_transf}).

Results

In quadriceps, known to be relatively preserved in GNEM, water T₂ at baseline was significantly higher compared to controls, and correlated strongly with the one-year evolution of Fat% and cCSA and R_{muscle_transf}. Various ³¹P NMRS indices showed significant differences in quadriceps and hamstring compared to controls and correlations existed between these indices and ΔFat%, ΔcCSA and R_{muscle_transf}.

Conclusions

This study demonstrates that disease activity indices such as water T₂ and ³¹P NMRS may predict disease progression in skeletal muscles of GNEM patients, and suggests that these measures may be considered to be valuable surrogate endpoints in the assessment of GNEM disease progression.

Simultaneous muscle water T2 and fat fraction mapping using transverse relaxometry with stimulated echo compensation

Skeletal muscle inflammation/necrosis and fat infiltration are strong indicators of disease activity and progression in many neuromuscular disorders. They can be assessed by muscle T2 relaxometry and water-fat separation techniques, respectively. In the present work, we exploited differences between water and fat T1 and T2 relaxivities by applying a bi-component extended phase graph (EPG) fitting approach to simultaneously quantify the muscle water T2 and fat fraction from standard multi-slice multi-echo (MSME) acquisitions in the presence of stimulated echoes. Experimental decay curves were adjusted to the theoretical model using either an iterative non-negative least-squares (NNLS) procedure or a pattern recognition approach. Twenty-two patients (age, 49 ± 18 years) were selected to cover a large range of muscle fat infiltration. Four cases of chronic or subchronic juvenile dermatomyositis (age, 8 ± 3 years) were investigated before and 3 months following steroid treatment. For control, five healthy volunteers (age, 25 ± 2 years) were recruited. All subjects underwent the MSME sequence and EPG fitting procedure. The EPG fitting algorithm allowed a precise estimation of water T2 and fat fraction in diseased muscle, even in the presence of large B1(+) inhomogeneities. In the whole cohort of patients, there was no overall correlation between water T2 values obtained with the proposed method and the fat fraction estimated inside muscle tissues (R(2) = 0.02). In the patients with dermatomyositis, there was a significant decrease in water T2 (-4.09 ± 3.7 ms) consequent to steroid treatment. The pattern recognition approach resulted in a 20-fold decrease in processing time relative to the iterative NNLS procedure. The fat fraction derived from the EPG fitting approach correlated well with the fat fraction derived from a standard three-point Dixon method (≈1.5% bias). The bi-component EPG fitting analysis is a precise tool to monitor muscle tissue disease activity and is able to handle bias introduced by fat infiltration and B1(+) inhomogeneities.