RF Heating Dependence of Head Model Positioning Using 4-Channel Parallel Transmission MRI and a Deep Brain Stimulation Construct
Bibliographic record
Abstract
Parallel radiofrequency transmission (pTx) continues to demonstrate promise in addressing magnetic resonance imaging (MRI) challenges at higher magnetic-field strengths, particularly regarding the safety of patients with implanted deep brain stimulation (DBS) devices. Radiofrequency (RF) shimming optimization methods have shown the potential of pTx to minimize DBS implant safety concerns relating to induced RF heating at 3T. This letter continues the assessment of 4-channel pTx technology and its associated “safe mode” for the DBS application. Safe mode sensitivity to patient setup mispositioning and movement is important and was studied in proof-of-concept. Phantom mispositioning can impact the electromagnetic near-field distribution and potentially affect the RF heating effects along an implanted DBS device. However, thermal simulations studying DBS patient head movements were performed and indicated minimal safety risks. These results were further validated by an MRI phantom mispositioning experiment encompassing the head motion studied in simulation. Temperature increases remained below +1°C for all tested scenarios in simulation and experiment. However, a severe pitch rotation in the experiment led to a +0.8°C increase, indicating that significant patient movement may still shift implanted DBS leads into higher risk zones. In conclusion, this letter further supports the potential of 4-channel pTx to address DBS patient safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".