Direct SAR mapping by thermoacoustic imaging: A feasibility study
Bibliographic record
Abstract
Purpose To develop a new method capable of directly measuring specific absorption rate (SAR) deposited in tissue using the thermoacoustic signal induced by short radiofrequency (RF) pulse excitation. Theory A detailed model based on the thermoacoustic wave generation and propagation is presented. Methods We propose a new concept for direct measurement of SAR, to be used as a safety assessment/monitoring tool for MRI. The concept involves the use of short bursts of RF energy and the measurement of the resulting thermoacoustic excitation pattern by an array of ultrasound transducers, followed by image reconstruction to yield the 3D SAR distribution. We developed a simulation framework to model this thermoacoustic SAR mapping concept and verified the concept in vitro. Results Simulations show good agreement between reconstructed and original SAR distributions with an error of 4.2, 7.2, and 8.4% of the mean SAR values in axial, sagittal, and coronal planes and support the feasibility of direct experimental mapping of SAR distributions in vivo. The in vitro experiments show good agreement with theory (r2 = 0.52). Conclusions A novel thermoacoustic method for in vivo mapping of local SAR patterns in MRI has been proposed and verified in simulation and in a phantom experiment. Magn Reson Med 78:1599–1606, 2017. © 2016 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".