Parallel radiofrequency transmission at 3 tesla to improve safety in bilateral implanted wires in a heterogeneous model
Bibliographic record
Abstract
PURPOSE: Elongated implanted conductors can interact with the radiofrequency (RF) transmission field during MRI, posing safety concerns of excessive heating in patients with deep brain stimulators. A technique using parallel RF transmission (pTx) is evaluated on an anthropomorphic heterogeneous model with bilateral and unilateral curved wires. METHODS: Amplitude and phase were optimized by simulation to minimize heating surrounding the implanted wires and to minimize B1+ inhomogeneity for four-channel and eight-channel pTx in a heterogeneous model. MRI experiments were conducted in an equivalent test phantom created from a common digital mesh file. RESULTS: In four-channel pTx, maximum local specific absorption rate (SAR) was reduced in both unilateral and bilateral wires by 47% and 59%, respectively, but with compromised B1+ homogeneity. Optimized eight-channel pTx substantially reduced local SAR compared with birdcage coil RF excitation in both unilateral and bilateral wires (reduction of maximum local SAR of 79% and 87%, respectively). B1+ inhomogeneity was limited to 17% and 26%, respectively. Experimental validation with four-channel pTx showed 80% and 92% temperature reduction at the tips of wire 1 and wire 2, respectively. CONCLUSION: This pTx approach yields promising reductions in local SAR at the tips of unilateral and bilateral implanted wires while maintaining image quality in simulation and experiment. Magn Reson Med 78:2408-2415, 2017. © 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".