An adaptive targeting algorithm for magnetic resonance‐guided high‐intensity focused ultrasound controlled hyperthermia
Bibliographic record
Abstract
BACKGROUND: Mild hyperthermia has been demonstrated to improve the efficacy of chemotherapy, radiation, and immunotherapy in various cancer types. One localized, non-invasive method of administering mild hyperthermia is magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU). However, challenges for ultrasound such as beam deflection, refraction and coupling issues may result in a misalignment of the HIFU focus and the tumor during hyperthermia. Currently, the best option is to stop the treatment, wait for the tissue to cool, and redo the treatment planning before restarting the hyperthermia. This current workflow is both time-consuming and unreliable. PURPOSE: An adaptive targeting algorithm was developed for MRgHIFU controlled hyperthermia treatments for cancer therapeutics. This algorithm executes in real time while hyperthermia is being administered to ensure that the focus is within our target region. If a mistarget is detected, the HIFU system will electronically steer the focus of the HIFU beam to the correct target. The goal of this study was to quantify the accuracy and precision of the adaptive targeting algorithm's ability to correct a purposely misplanned hyperthermia treatment in real-time using a clinical MRgHIFU system. METHODS: A gelatin phantom with acoustic properties matched to the average speed of sound in human tissue was used to test the adaptive targeting algorithm's accuracy and precision. The target was purposely offset 10 mm away from the focus at the origin, in four orthogonal directions, allowing the algorithm to correct for this mistarget. In each direction, 10 data sets were collected for a total sample size of 40. Hyperthermia was administered with a target temperature set at 42°C. The adaptive targeting algorithm was run during the hyperthermia treatment and 20 thermometry images were collected after the beam steering occurred. The location of the focus was quantified by calculating the center of heating on the MR thermometry data. RESULTS: The average calculated trajectory passed to the HIFU system was 9.7 mm ± 0.4 mm where the target trajectory was 10 mm. The accuracy of the adaptive targeting algorithm after the beam steering correction was 0.9 mm and the precision was 1.6 mm. CONCLUSION: The adaptive targeting algorithm was implemented successfully and was able to correct the 10 mm mistargets with high accuracy and precision in gelatin phantoms. The results demonstrate the capability to correct the MRgHIFU focus location during controlled hyperthermia.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".