Metal Artifact Reduction Sequence: Early Clinical Applications
Bibliographic record
Abstract
Artifact arising from metal hardware remains a significant problem in orthopedic magnetic resonance imaging. The metal artifact reduction sequence (MARS) reduces the size and intensity of susceptibility artifacts from magnetic field distortion. The sequence, which is based on view angle tilting in combination with increased gradient strength, can be conveniently used in conjunction with any spin-echo sequence and requires no additional imaging time. In patients with persistent pain after femoral neck fracture, the MARS technique allows visualization of marrow adjacent to hip screws, thus enabling diagnosis or exclusion of avascular necrosis. Other applications in the hip include assessment of periprosthetic soft tissues after hip joint replacement surgery, postoperative assessment after resection of bone tumors and reconstruction, and localization of unopacified methyl methacrylate cement prior to hip arthroplasty revision surgery. In the knee, the MARS technique allows visualization of structures adjacent to implanted metal staples, pins, or screws. The technique can significantly improve visualization of periprosthetic bone and soft-tissue structures even in patients who have undergone total knee arthroplasty. In patients with spinal fixation hardware, the MARS technique frequently allows visualization of the vertebral bodies and spinal canal contents. The technique can be helpful after wrist fusion or screw fixation of scaphoid fractures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".