Simultaneous Visualization of Nerves and Vessels of the Lower Extremities Using Magnetization-Prepared Susceptibility Weighted Magnetic Resonance Imaging at 3.0 T
Bibliographic record
Abstract
BACKGROUND: Identifying the extent of involvement of the vessel and nerve, particularly in regard to preoperative evaluation and precise localization of the tumor and its relation to the structures of the extremities, has important applications for advancing the treatment of lower extremity diseases. OBJECTIVE: To review the technical feasibility of simultaneous visualization of nerves and vessels of the lower extremities by using magnetization-prepared susceptibility-weighted magnetic resonance (MR) imaging (MP-SWI) at 3.0T. METHODS: Ten healthy volunteers and 10 patients were studied. Optimized MP-SWI, MR neurography (MRN) based on 3D diffusion-weighted steady-state free precession imaging and contrast-enhanced MR angiography (CE-MRA) sequences were performed for each subject. The means of signal-to-noise ratio (SNR)n, SNRv, SNRm, contrast-to-noise ratio (CNR)n,m and CNRv,m were calculated and the certainty of identifying nerves and vessels was determined. CNRn,m between MP-SWI and MRN, and CNRv,m between MP-SWI and CE-MRA were compared. RESULTS: MP-SWI provides slightly poorer CNRv,m than CE-MRA, whereas MP-SWI provides a better CNRn,m than MRN. In thin-slice-thickness maximum-intensity projection arbitrary planes, the sciatic nerve and its branches were clearly identified (score 1 or 2 of 2) in 17 subjects (85%); the femoral artery and the main branches were identified (score 1 or 2 of 2) in all 20 subjects (100%). The nerves are isointense to slightly hypointense to muscle, and the vessels show a more obvious hyperintense signal than muscle in MP-SWI. CONCLUSION: The proposed MP-SWI demonstrates the feasibility of simultaneously visualizing nerves and vessels of the lower extremities without using an exogenous contrast agent. It may enable straightforward localization of a disease process to a specific nerve and vessel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".