ArticleFrontiers in neurology2021
Fast Open-Source Toolkit for Water T2 Mapping in the Presence of Fat From Multi-Echo Spin-Echo Acquisitions for Muscle MRI.
Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Dynamic MR of Muscle Contraction During Electrical Muscle Stimulation: Potential Application to the Evaluation of Neuromuscular Diseases.NMR in biomedicine · 2025Article
- Enhancement of muscle activation during squat exercise: evaluation with magnetic resonance imaging.European journal of applied physiology · 2025Article
- Nucleolar FRG2 lncRNAs inhibit rRNA transcription and cytoplasmic translation, linking FSHD to dysregulation of muscle-specific protein synthesis.Nucleic acids research · 2025Article
- Whole liver phase-based R2 mapping in liver iron overload within a breath-hold.Magnetic resonance in medicine · 2025Article
- MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour.Magma (New York, N.Y.) · 2025Review
- Improving Accuracy and Reproducibility of Cartilage TJournal of magnetic resonance imaging : JMRI · 2025Article
- Natural history of facioscapulohumeral muscular dystrophy evaluated by multiparametric quantitative MRI: a prospective cohort study.Journal of neurology · 2025Article
- Multiparametric MRI Assessment of Morpho-Functional Muscle Changes Following a 6-Month FES-Cycling Training Program: Pilot Study in People With a Complete Spinal Cord Injury.JMIR rehabilitation and assistive technologies · 2025Article
- A 5-year natural history study in LAMA2-related muscular dystrophy and SELENON-related myopathy: the Extended LAST STRONG study.BMC neurology · 2024Observational
- Magnetic Resonance Imaging Biomarkers of Muscle.Tomography (Ann Arbor, Mich.) · 2024Review
- AI driven analysis of MRI to measure health and disease progression in FSHD.Scientific reports · 2024Article
- A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.Scientific reports · 2024Article
- Assessment of training-associated changes of the lumbar back muscle using a multiparametric MRI protocol.Frontiers in physiology · 2024Article
- Review
- Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease.Frontiers in neurology · 2023Article
- Dynamic magnetic resonance imaging of muscle contraction in facioscapulohumeral muscular dystrophy.Scientific reports · 2022Article
- Longitudinal Quantitative MRI Evaluation of Muscle Involvement in Amyotrophic Lateral Sclerosis.Frontiers in neurology · 2021Article
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Authors and funding
12 authors at 6 institutions in 4 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Imaging has become a valuable tool in the assessment of neuromuscular diseases, and, specifically, quantitative MR imaging provides robust biomarkers for the monitoring of disease progression. Quantitative evaluation of fat infiltration and quantification of the T2 values of the muscular tissue's water component (wT2) are two of the most essential indicators currently used. As each voxel of the image can contain both water and fat, a two-component model for the estimation of wT2 must be used. In this work, we present a fast method for reconstructing wT2 maps obtained from conventional multi-echo spin-echo (MESE) acquisitions and released as Free Open Source Software. The proposed software is capable of fast reconstruction thanks to extended phase graphs (EPG) simulations and dictionary matching implemented on a general-purpose graphic processing unit. The program can also perform more conventional biexponential least-squares fitting of the data and incorporate information from an external water-fat acquisition to increase the accuracy of the results. The method was applied to the scans of four healthy volunteers and five subjects suffering from facioscapulohumeral muscular dystrophy (FSHD). Conventional multi-slice MESE acquisitions were performed with 17 echoes, and additionally, a 6-echo multi-echo gradient-echo (MEGE) sequence was used for an independent fat fraction calculation. The proposed reconstruction software was applied on the full datasets, and additionally to reduced number of echoes, respectively, to 8, 5, and 3, using EPG and biexponential least-squares fitting, with and without incorporating information from the MEGE acquisition. The incorporation of external fat fraction maps increased the robustness of the fitting with a reduced number of echoes per datasets, whereas with unconstrained fitting, the total of 17 echoes was necessary to retain an independence of wT2 from the level of fat infiltration. In conclusion, the proposed software can successfully be used to calculate wT2 maps from conventional MESE acquisition, allowing the usage of an optimized protocol with similar precision and accuracy as a 17-echo acquisition. As it is freely released to the community, it can be used as a reference for more extensive cohort studies.
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