Simulation of high intensity focused ultrasound ablation to enable ultrasound thermal monitoring
Bibliographic record
Abstract
High Intensity Focused Ultrasound (HIFU) is a non-invasive ablative therapy. It is usually performed under MR monitoring, which provides reliable real-time thermal information to ensure a complete tumor ablation while preserving as much healthy tissue as possible. Unfortunately, many patients do not necessarily have access to this expensive and cumbersome cutting-edge technology, which is prohibitive for a widespread use of MRI to guide thermal ablation procedures. Ultrasound (US) is a promising low cost and portable alternative, that allows real-time monitoring and can easily be deployed outside hospitals. However, US-based thermometry alone is not robust enough for the monitoring of in-vivo tissue ablation, and its feasibility is demonstrated only on in-vitro cases for small range of temperatures, up to 50°C. Computational models can simulate the biophysical phenomena and mechanisms which govern this complex thermal therapy. The US wave propagation, the temperature evolution as well as the resulted necrotic lesion can be modeled. A method integrating those sources of information to intra-operative US data would allow to recover the accurate temperature in a wider range. Therefore, US thermometry could be improved and provide an inexpensive yet comprehensive method for intra-procedural monitoring of the ablative process through HIFU. In this paper, we propose to study the rise in temperature induced by high intensity US propagation in biological tissue, which is particularly difficult to simulate due to the complexity of the involved phenomena. The physics-based HIFU model simulates the nonlinear US propagation using a k-space model coupled with the heat propagation in biological tissue using a reaction-diffusion equation. We analyze numerically the model to evaluate its accuracy and related computational cost. Finally, our simulation approach is validated against MR thermometry, the gold-standard monitoring tool used in clinical setting. Three consecutive HIFU ablations were performed on a 2% agar and 2% silicon phantom using the Sonalleve V2 MR-HIFU system (Profound Medical, Toronto, Canada).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".