Real-Time Reconstruction of HIFU Focal Temperature Field Based on Deep Learning
Bibliographic record
Abstract
Objective and Impact Statement : High-intensity focused ultrasound (HIFU) therapy is a promising noninvasive method that induces coagulative necrosis in diseased tissues through thermal and cavitation effects, while avoiding surrounding damage to surrounding normal tissues. Introduction : Accurate and real-time acquisition of the focal region temperature field during HIFU treatment marked enhances therapeutic efficacy, holding paramount scientific and practical value in clinical cancer therapy. Methods : In this paper, we initially designed and assembled an integrated HIFU system incorporating diagnostic, therapeutic, and temperature measurement functionalities to collect ultrasound echo signals and temperature variations during HIFU therapy. Furthermore, we introduced a novel multimodal teacher–student model approach, which utilizes the shared self-expressive coefficients and the deep canonical correlation analysis layer to aggregate each modality data, then through knowledge distillation strategies, transfers the knowledge from the teacher model to the student model. Results : By investigating the relationship between the phantoms, in vitro, and in vivo ultrasound echo signals and temperatures, we successfully achieved real-time reconstruction of the HIFU focal 2D temperature field region with a maximum temperature error of less than 2.5 °C. Conclusion : Our method effectively monitored the distribution of the HIFU temperature field in real time, providing scientifically precise predictive schemes for HIFU therapy, laying a theoretical foundation for subsequent personalized treatment dose planning, and providing efficient guidance for noninvasive, nonionizing cancer treatment.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".