MRI Monitoring of HIFU Heat Deposition Dynamics
Bibliographic record
Abstract
Successful HIFU surgery demands accurate and reliable temperature monitoring. Heating affects acoustic properties of biological tissues changing the acoustic field and consequent heat deposition. As a result, HIFU thermal lesions grow asymmetrically from the focus toward the transducer and perpendicularly to the beam axis. If the growth process is not monitored, the healthy tissues located on the beam path can be damaged. We propose an improved approach to MRI temperature mapping designed for the real‐time guidance of HIFU surgery, which is based on the acquisition of images along the HIFU beam and allows monitoring and analyzing heat deposition and distribution patterns. Our method visualizes the heat deposition site and shows its evolution during the surgery. In‐vitro HIFU experiments have been performed under real‐time MRI temperature monitoring using the Proton Resonance Frequency Shift method. A set of mathematical operations has been applied to MRI temperature maps, which revealed the HIFU thermal focus (which not always coincides with the geometrical focus) as well as the focus’ shape, size and displacements during sonications. The operations also visualized the HIFU beam waist as well as the hot area borders and main heat transfer directions. Our approach provides information for on‐line prevention of undesirable heat deposition resulting from intra‐operative changes of tissue acoustic properties. Since making small sharp HIFU lesions demands the usage of short high‐power sonications, predicting the thermal lesion growth resulting from the next sonication becomes highly desirable. Our method has a potential to supply the information necessary for such predictions, which can increase the safety and improving the clinical outcome of the HIFU surgery.
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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 | 0.001 |
| 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) | 0.001 | 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".