Optimized radiofrequency shimming using low-heating B1+-mapping in the presence of deep brain stimulation implants: Proof of concept
Bibliographic record
Abstract
MRI of patients with Deep Brain Stimulation (DBS) implants is constrained due to radiofrequency (RF) heating of the implant lead. However, "RF-shimming" parallel transmission (PTX) has the potential to reduce DBS heating during MRI. As part of using PTX in such a "safe mode", maps of the RF transmission field (B1+) are typically acquired for calibration purposes, with each transmit coil excited individually. These maps often have large zones of low signal intensity distant from the specific coil that is being excited, raising concerns that low signal-to-noise ratio (SNR) in these zones might negatively impact the ability of the optimized RF shim settings to suppress heating in safe mode. One way to improve SNR would be to increase RF transmission power during B1+ mapping, but this also raises heating concerns especially for coil elements proximal to the implant. Acting with an abundance of caution, it would be useful to investigate methods that permit B1+ mapping with low localized heating while producing high SNR measurements that lead to safe PTX RF shim settings. The present work addresses this issue in proof of concept using electromagnetic simulations and experimental PTX MRI. A two-step optimization algorithm is proposed and examined for a cylindrical phantom with an implanted wire to enable 1) robust B1+ mapping with low localized heating; and 2) robust RF shimming PTX with low localized heating and good B1+ homogeneity over a large imaging volume. Simulation and experimental outcomes were compared with those obtained using an existing simulation-driven workflow for obtaining safe mode RF shim settings, and for quadrature RF transmission using a circularly polarized (CP) birdcage head coil. Experimental results showed that although both existing and proposed safe-mode workflows effectively suppressed localized heating at the wire tip in comparison to the CP coil results, the proposed workflow produced much smaller temperature elevations and much improved signal uniformity. These promising results support continued investigation and refinement of the proposed workflow, involving more realistic scenarios toward ultimate implementations in DBS patients.
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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 | 0.000 |
| 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".