ReviewNeural regeneration research2017
Quantification of intermuscular and intramuscular adipose tissue using magnetic resonance imaging after neurodegenerative disorders.
Review in Neural regeneration research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04273828 (Impact of Muscle Degeneration in Chronic Ow Back Pain in Patients Undergoing Neural Decompression), which is not on this map. Cited by 43 papers, 2 of them syntheses 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.
Impact of Muscle Degeneration in Chronic Ow Back Pain in Patients Undergoing Neural Decompression
Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automatability and validity of methods for the quantification of intra-/Intermuscular adipose tissue in conventional MRI: a systematic review.BMC medical imaging · 2025Pooled it
- Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution.Nature metabolism · 2025Guideline
- Effects of 8 weeks of bed rest with or without resistance exercise intervention on the volume of the muscle tissue and the adipose tissues of the thigh.Physiological reports · 2020Trial
- Prediction of thigh skeletal muscle mass using dual energy x-ray absorptiometry compared to magnetic resonance imaging after spinal cord injury.The journal of spinal cord medicine · 2019Trial
- Lumbar Muscle Morphology Correlates With Early Surgical Outcomes in Adolescent Idiopathic Scoliosis: A Pilot Study.Global spine journal · 2026Article
- Lumbar muscle morphology and composition in adolescent idiopathic scoliosis: a magnetic resonance study.Spine deformity · 2026Article
- Deep learning-based framework for comprehensive quantification of thigh and calf muscles and adipose tissues from MRI.Magma (New York, N.Y.) · 2026Article
- Epimuscular fat and its clinical relevance: a narrative review focused on paraspinal muscles.Quantitative imaging in medicine and surgery · 2026Review
- Article
- Poor muscle quality: A hidden and detrimental health condition in obesity.Reviews in endocrine & metabolic disorders · 2025Review
- Reference Values for Water-Specific T1, Intermuscular and Intramuscular Fat Content in Skeletal Muscle at 2.89 T.Journal of magnetic resonance imaging : JMRI · 2025Observational
- Fully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python.Skeletal muscle · 2024Article
- Myosteatosis: diagnostic significance and assessment by imaging approaches.Quantitative imaging in medicine and surgery · 2024Review
- Statin use and longitudinal changes in quantitative MRI-based biomarkers of thigh muscle quality: data from Osteoarthritis Initiative.Skeletal radiology · 2024Article
- Probiotics as Potential Therapeutic Agents: Safeguarding Skeletal Muscle against Alcohol-Induced Damage through the Gut-Liver-Muscle Axis.Biomedicines · 2024Review
- Myosteatosis: Diagnosis, pathophysiology and consequences in metabolic dysfunction-associated steatotic liver disease.JHEP reports : innovation in hepatology · 2024Review
- Advancements in sarcopenia diagnosis: from imaging techniques to non-radiation assessments.Frontiers in medical technology · 2024Review
- Uremic sarcopenia: the role of intramuscular adipose tissue as a potential early identifier.Frontiers in medicine · 2024Article
- Review
- Denervation impacts muscle quality and knee bone mineral density after spinal cord injury.Spinal cord · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ectopic adiposity has gained considerable attention because of its tight association with metabolic and cardiovascular health in persons with spinal cord injury (SCI). Ectopic adiposity is characterized by the storage of adipose tissue in non-subcutaneous sites. Magnetic resonance imaging (MRI) has proven to be an effective tool in quantifying ectopic adiposity and provides the opportunity to measure different adipose depots including intermuscular adipose tissue (IMAT) and intramuscular adipose tissue (IntraMAT) or intramuscular fat (IMF). It is highly important to distinguish and clearly define these compartments, because controversy still exists on how to accurately quantify these adipose depots. Investigators have relied on separating muscle from fat pixels based on their characteristic signal intensities. A common technique is plotting a threshold histogram that clearly separates between muscle and fat peaks. The cut-offs to separate between muscle and fat peaks are still not clearly defined and different cut-offs have been identified. This review will outline and compare the Midpoint and Otsu techniques, two methods used to determine the threshold between muscle and fat pixels on T1 weighted MRI. The process of water/fat segmentation using the Dixon method will also be outlined. We are hopeful that this review will trigger more research towards accurately quantifying ectopic adiposity due to its high relevance to cardiometabolic health after SCI.
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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.