Magnetic Resonance Thermometry of Focused Ultrasound Using a Preclinical Focused Ultrasound Robotic System at 3T
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it