Quantitative evaluation of metal artifact reduction techniques
Bibliographic record
Abstract
PURPOSE: To develop a technique to quantify artifact, and to use it to compare the effectiveness of several approaches to metal artifact reduction, including view angle tilting and increasing the slice select and image bandwidths (BWs), in terms of metal artifact reduction, noise, and blur. MATERIALS AND METHODS: Nonmetallic replicas of two metal implants (stainless steel and titanium/chromium-cobalt femoral prostheses) were fabricated from wax, and MR images were obtained of each component immersed in water. The differences between the images of each metal prosthesis and its wax counterpart were measured. The contributions from noise and blur were isolated, resulting in a measure of the metal artifact. Several off-resonance artifact reduction techniques were assessed in terms of metal artifact reduction capability, as well as signal to noise ratio and blur. RESULTS: Increasing the image BW from +/-16 kHz to +/-64 kHz was found to reduce the artifact by an average of 60%, while employing view angle tilting (VAT) alone was found to reduce the artifact by an average of 63%. The metal artifact reduction sequence (MARS), which combines several susceptibility artifact reduction techniques, resulted in the least amount of image distortion, reducing the artifact by an average of 79%. CONCLUSION: The results indicate that while VAT alone (with an image BW of +/-16 kHz) resulted in the smallest amount of total energy and no reduction in the signal-to-noise ratio compared to a conventional spin-echo pulse sequence, MARS resulted in significantly less artifact and dramatically less blur.
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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 | 0.005 | 0.014 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".