Fully phase‐encoded MRI near metallic implants using ultrashort echo times and broadband excitation
Bibliographic record
Abstract
Purpose To develop a fully phase‐encoded MRI method for distortion‐free imaging near metallic implants, in clinically feasible acquisition times. Theory and Methods An accelerated 3D fully phase‐encoded acquisition with broadband excitation and ultrashort echo times is presented, which uses a broadband radiofrequency pulse to excite the entire off‐resonance induced by the metallic implant. Furthermore, fully phase‐encoded imaging is used to prevent distortions caused by frequency encoding, and to obtain ultrashort echo times for rapidly decaying signal. Results Phantom and in vivo acquisitions were used to describe the relationship among excitation bandwidth, signal loss near metallic implants, and T1 weighting. Shorter radiofrequency pulses captured signal closer to the implant by improving spectral coverage and allowing shorter echo times, whereas longer pulses improved T1 weighting through larger maximum attainable flip angles. Comparisons of fully phase‐encoded acquisition with broadband excitation and ultrashort echo times to T1‐weighted multi‐acquisition with variable resonance image combination selective were performed in phantoms and subjects with metallic knee and hip prostheses. These acquisitions had similar contrast and acquisition efficiency. Conclusions Accelerated fully phase‐encoded acquisitions with ultrashort echo times and broadband excitation can generate distortion free images near metallic implants in clinically feasible acquisition times. Magn Reson Med 79:2156–2163, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".