Magnetic Resonance Thermometry of Focused Ultrasound Using a Preclinical Focused Ultrasound Robotic System at 3T
Bibliographic record
Abstract
Aim: Focused ultrasound (FUS) therapies are often performed within magnetic resonance imaging (MRI) systems providing thermometry-based temperature monitoring. Herein, MRI thermometry was assessed for FUS sonications executed using a preclinical system on agar-based phantoms at 1.5T and 3T MRI scanners, using the proton resonance frequency shift technique. Materials and Methods: Sonications were executed at 1.5T and 3T to assess the system and observe variations in magnetic resonance (MR) thermometry temperature measurements. MR thermometry was assessed at 3T, for identical sonications on three agar-based phantoms doped with varied silica and evaporated milk concentrations, and for sonications executed at varied acoustic power of 1.5-45 W. Moreover, echo time (TE) values of 5-20 ms were used to assess the effect on the signal-to-noise ratio (SNR) and temperature change sensitivity. Results: Clearer thermal maps with a 2.5-fold higher temporal resolution were produced for sonications at 3T compared to 1.5T, despite employment of similar thermometry sequences. At 3T, temperature changes between 41°C and 50°C were recorded for the three phantoms produced with varied silica and evaporated milk, with the addition of 2% w/v silica resulting in a 20% increase in temperature change. The lowest acoustic power that produced reliable beam detection within a voxel was 1.5 W. A TE of 10 ms resulted in the highest temperature sensitivity with adequate SNR. Conclusions: MR thermometry performed at 3T achieved short temporal resolution with temperature dependencies exhibited with the sonication and imaging parameters. Present data could be used in preclinical MRI-guided FUS feasibility studies to enhance MR thermometry.
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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 | 0.001 |
| 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 | 0.001 | 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".