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Record W4416588665 · doi:10.1055/s-0045-1811387

Quantitative muscle MRI analysis of morphological changes induced by blood flow restriction training – a prospective pilot study

2025· article· W4416588665 on OpenAlexaff
Antonino De Lorenzo, L Butry, Johannes Forsting, Martijn Froeling, Arthur Praetorius, C Raeder, Robert Rehmann, Tobias Ruck, Christian Schoepp, Janina Tennler, Lara Schlaffke

Bibliographic record

Venuephysioscience · 2025
Typearticle
Language
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsAthletic Edge Sports Medicine
Fundersnot available
KeywordsBlood flow restrictionBlood flowProspective cohort studyMagnetic resonance imagingHemodynamics

Abstract

fetched live from OpenAlex

Introduction Low-load blood flow restriction (BFR) resistance training is an innovative training method with equivalent increases in strength as traditional high-load resistance training. While immediate physiological changes have been described, biomarkers for BFR resistance training induced muscle morphology changes are needed to assess its effect mechanisms and safety in healthy and various patient populations. The aim of this pilot study is to identify quantitative magnetic resonance imaging (qMRI) biomarkers of BFR resistance training. Methods A prospective pilot study with 3 participants was conducted. Each participant received a 5-week knee extension training program, including 3 sessions per week. One leg was trained with BFR (60% limb occlusion pressure) using 6 sets until muscle failure at 40% of the repetition maximum (RM). The other leg was trained without BFR with 3 sets at 15RM. At baseline and at the end of the training program, qMRI of a 15 cm section of the thighs was acquired, including T2 mapping, DIXON, and diffusion-weighted imaging to assess oedematous tissue alterations, intramuscular fat fraction, and muscle architecture, respectively. QMRITools was used to process qMRI data. Results The volume-equated mean mechanical training load was 16676±2289 kg for the BFR and 16913±2775 kg for the non-BFR leg. Change from baseline in water T2 relaxation time (BFR: 0.33 ms [0.5%], non-BFR: 0.34 ms [0.9%]) and fat fraction (BFR: 0.09% [4%], non-BFR: -0.19% [–7%]) were within the measurement error. An increase in muscle volume (BFR: 421 ccm [31%], non-BFR: 430 ccm [30%]) and a decrease in fractional anisotropy (BFR: -0.01 [–5%], non-BFR: -0.008 [4%]) with stable mean diffusivity (BFR: -0.0004x10-3 mm2/s [–0.03%], non-BFR: -0.01x10-3 mm2/s [-0.8%]) and radial diffusivity (BFR: 0.005x10-3 mm2/s [0.4%], non-BFR: -0.003x10-3 mm2/s [0.2%]) were observed. Conclusion The BFR and non-BFR leg showed similar signs of hypertrophy in the M. quadriceps femoris without inflammatory processes, indicating a safe use of BFR resistance training in future studies and rehabilitation programs, as it achieves these results with lower individual joint loads. To evaluate the effect mechanisms of BFR resistance training, a sham-BFR group will be included in the full study. Adaptions to patients with hereditary or inflammatory neuromuscular disorders need to be evaluated carefully. Publication History Article published online: 24 November 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.334
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
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