Potential Role of MRI Imaging for Myofascial Pain: A Scoping Review for the Clinicians and Theoretical Considerations
Bibliographic record
Abstract
The most common cause of chronic musculoskeletal pain is chronic myofascial pain syndrome (MPS). MPS often presents with increased muscle stiffness, and the myofascial trigger point (MTrP). Imaging modalities have been used to identify the MTrP, but their role in the detection and diagnosis of MPS remains unclear. The purpose of this review was to identify evidence in literature for the use of imaging in the role of classifying and explaining the physiology of MTrPs. Since few imaging techniques have been performed on MTrPs, we explored the imaging techniques that can effectively image complex skeletal muscle microstructure, and how they could be used. As part of a scoping review, we conducted a systematic search from three medical databases (CINAHL, EMBASE and MEDLINE) from year to year to analyze past MTrP imaging, as well as analyzing imaging techniques performed on the microstructure of muscle. Previously, ultrasound has been used to differentiate active, latent MTrPs, but these studies do not adequately address their underlying anatomical structure. MRI remains the standard method of imaging skeletal muscle. The existing MRI literature suggests that the DTI technique can quantify muscle injury, strain, and structure. However, theoretically, HARDI and DKI techniques seem to provide more information for complex structural areas, although these modalities have a disadvantage of longer scan times and have not been widely used on skeletal muscle. Our review suggests that DTI is the most effective imaging modality that has been used to define the microstructure of muscle and hence, could be optimal to image the MTrP. HARDI and DKI are techniques with theoretical potential for analysis of muscle, which may provide more detailed information representative of finer muscle structural features. Future research utilizing MRI techniques to image muscle are necessary to provide a more robust means of imaging skeletal muscle and the MTrP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".