Quantitative imaging techniques are emerging in the field of magnetic resonance imaging of neuromuscular diseases (NMD). T 2 of water (T 2w ) is considered an important imaging marker to assess acute and chronic alterations of the muscle fibers, being generally interpreted as an indicator for "disease activity" in the muscle tissue.To validate the accuracy and robustness of quantitative imaging methods, 1 H magnetic resonance spectroscopy (MRS) can be used as a gold standard.The purpose of the present work was to investigate T 2w of remaining muscle tissue in regions of higher proton density fat fraction (PDFF) in 40 patients with defined NMD using multi-TE single-voxel 1 H MRS. Patients underwent MR measurements on a 3 T system to perform a multi-TE single-voxel stimulated echo acquisition method (STEAM) MRS (TE = 11/15/20/25(/35) ms) in regions of healthy, edematous and fatty thigh muscle tissue. Muscle regions for MRS were selected based on T 2weighted water and fat images of a two-echo 2D Dixon TSE.MRS results were confined to regions with qualitatively defined remaining muscle tissue without edema and high fat content, based on visual grading of the imaging data.The results showed decreased T 2w values with increasing PDFF with R 2 = 0.45 (p < 10 −3 ) (linear fit) and with R 2 = 0.51 (exponential fit).The observed dependence of T 2w on PDFF should be considered when using T 2w as a marker in NMD imaging and when performing single-voxel MRS for T 2w in regions enclosing edematous, nonedematous and fatty infiltrated muscle tissue. KEYWORDS fatty infiltration, imaging marker, MRS, NMD, PDFF, quantitative imaging techniques, skeletal muscle, T 2 of water Abbreviations used: ALS, amyotrophic lateral sclerosis; CNM, centronuclear myopathy; DM2, myotonic dystrophy type 2; EPG, extended phase graph; FSHD, facioscapulohumeral muscular dystrophy; GRASE, gradient and spin echo; IBM, inclusion body myositis; LGMD2A, limb girdle muscular dystrophy type 2A; LGMD2I, limb girdle muscular dystrophy type 2I; LGMD2L, limb girdle muscular dystrophy type 2L; MESE, multi-echo spin-echo; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; NMD, neuromuscular diseases; PDFF, proton density fat fraction; STEAM, stimulated echo acquisition method; T2f, T2 of fat; T2w, T2 of water; TSE, turbo spin-echo; VOI, volume of interest

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Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

Identification of this additional patient from a distant part of the originally described pedigree (Synofzik et al. 2014) confirms pathogenicity of DNAJC3 mutations. Hypothyroidism is a newly identified feature in addition to the known phenotype (diabetes with multisystemic neurodegeneration).