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5 results for “Quantitative Muscle MRI”
DATASET RELATED TO ARTICLE "Muscle quantitative MRI in adult SMA patients on nusinersen treatment_ a longitudinal study"
<p>Excel file with raw data of muscle fat fraction before and after nusinersen treatment</p>
Data from: Lower extremity muscle pathology in myotonic dystrophy type 1 assessed by quantitative MRI
Objective: To determine the value of quantitative MRI to provide imaging biomarkers for disease in 20 different upper and lower leg muscles of myotonic dystrophy type 1 (DM1) patients. Methods: We acquired images covering these muscles in 33 genetically and clinically well-characterized DM1 patients and 10 unaffected controls. MR images were recorded with a Dixon method to determine muscle fat fraction, muscle volume and contractile muscle volume, and a multi-spin echo sequence to determine T2 water relaxation time (T2water), reflecting putative oedema. Results: Muscles in DM1 patients had higher fat fractions than muscles of controls (15.6%±11.1% vs. 3.7%±1.5%). Also, patients had smaller muscle volumes (902±232 cm3 vs. 1097±251 cm3), contractile muscle volumes (779±247 cm3 vs. 1054±246 cm3), and increased T2water (33.4±1.0 ms vs. 31.9±0.6 ms), indicating atrophy and oedema, respectively. Lower leg muscles were affected most frequently, especially the gastrocnemius medialis and soleus. Distribution of fat content per muscle indicated gradual fat infiltration in DM1. Between-patient variation in fat fraction was explained by age (~45%), and another ~14% by estimated progenitor CTG repeat length (r2 = 0.485) and somatic instability (r2 = 0.590). Fat fraction correlated with the six-minute walk test (r = -0.553) and muscular impairment rating scale (r = 0.537), and revealed subclinical muscle involvement. Conclusion: This cross-sectional quantitative MRI study of 20 different lower extremity muscles in DM1 patients revealed abnormal values for muscle fat fraction, volume and T2water, which therefore may serve as objective biomarkers to assess disease state of skeletal muscles in these patients.
Data from: Lower extremity muscle pathology in myotonic dystrophy type 1 assessed by quantitative MRI
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Dataset related to article "Quantitative Muscle MRI Protocol as Possible Biomarker in Becker Muscular Dystrophy"
<p>The database contains descriptive tables with clinical scores and quantitative MRI parameters values extracted from the thigh and the calf of the subjects involve in the study. A comparison table with the statistical correlation is also reported.</p>
Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy
<p><strong>Introduction</strong>. This database includes the radiomic features used as covariates to train machine learning algorithms in the paper “ Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy”.</p> <p><strong>Purpose</strong>. Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning.</p> <p><strong>Method</strong>. Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset.</p> <p><strong>Results</strong>. Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2.</p> <p><strong>Conclusion</strong>. This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.</p>
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