Trial reportMagnetic resonance imaging2011
Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities.
Trial report in Magnetic resonance imaging, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability.PloS one · 2018Pooled it
- Comparison of Longitudinal Skeletal Thigh Muscle Findings With Magnetic Resonance Imaging in Patients With Peripheral Artery Disease With-Versus-Without Diabetes Mellitus.The American journal of cardiology · 2022Trial
- Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.PloS one · 2018Trial
- Trial
- Intra- and inter-rater reliability of skeletal muscle mass MRI assessment among adults: a systematic review.Quantitative imaging in medicine and surgery · 2026Review
- Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.Scientific reports · 2025Article
- BioCompNet: A Deep Learning Workflow Enabling Automated Body Composition Analysis toward Precision Management of Cardiometabolic Disorders.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease.The American journal of cardiology · 2024Article
- A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging.Magnetic resonance imaging · 2024Article
- Relation of Magnetic Resonance Imaging Based Arterial Signal Enhancement to Markers of Peripheral Artery Disease.The American journal of cardiology · 2021Article
- Overview of MR Image Segmentation Strategies in Neuromuscular Disorders.Frontiers in neurology · 2021Review
- Automated multi-atlas segmentation of gluteus maximus from Dixon and T1-weighted magnetic resonance images.Magma (New York, N.Y.) · 2020Article
- Muscle mass assessment in renal disease: the role of imaging techniques.Quantitative imaging in medicine and surgery · 2020Review
- Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine.BMC musculoskeletal disorders · 2019Article
- Observational
- Calf muscle perfusion as measured with magnetic resonance imaging to assess peripheral arterial disease.Medical & biological engineering & computing · 2016Article
- Segmentation and quantification of adipose tissue by magnetic resonance imaging.Magma (New York, N.Y.) · 2016Review
- Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system.European radiology · 2014 · on this mapArticle
- Quantitative proton MR techniques for measuring fat.NMR in biomedicine · 2013Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Muscle volume measurements are essential for an array of diseases ranging from peripheral arterial disease, muscular dystrophies, neurological conditions to sport injuries and aging. In the clinical setting, muscle volume is not routinely measured due to the lack of standardized ways for its repeatable quantification. In this paper, we present magnetic resonance muscle quantification (MRMQ), a method for the automatic quantification of thigh muscle volume in magnetic resonance imaging (MRI) scans. MRMQ integrates a thigh segmentation and nonuniform image gradient correction step, followed by feature extraction and classification. The classification step leverages prior probabilities, introducing prior knowledge to a maximum a posteriori classifier. MRMQ was validated on 344 slices taken from 60 MRI scans. Experiments for the fully automatic detection of muscle volume in MRI scans demonstrated an averaged accuracy, sensitivity and specificity for leave-one-out cross-validation of 88.3%, 93.6% and 87.2%, respectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.