MRI of the Brachial Plexus: Modified Imaging Technique Leading to a Better Characterization of Its Anatomy and Pathology
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of the brachial plexus due to its superior soft tissue resolution and multiplanar capabilities. The evaluation of the brachial plexus however represents a diagnostic challenge for the clinician and the radiologist. The imaging assessment of the brachial plexus, in particular, has been traditionally challenging due to the complexity of its anatomy, its distribution in space and due to technical factors. Herein, we describe a modified technique used in our institution for the evaluation of the brachial plexus which led to a substantial decrease in scanning time and to better visualization of all the segments of the brachial plexus from the roots to the branches, in only one or two images, facilitating therefore the understanding of the anatomy and the interpretation of the study. To our knowledge, we are the first group to describe this technique of imaging the brachial plexus. We illustrate the benefit of this modified technique with an example of a patient with a lesion in the proximal branches of the left brachial plexus that was clinically suspected but missed on conventional brachial plexus imaging for six consecutive years. In addition, we review the common and infrequent benign and malignant pathology that can affect the brachial plexus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".