Real-Time Motion Compensation of Magnetic Resonance Thermometry and Adaptive Targeting Algorithms for Focused Ultrasound Controlled Hyperthermia in Sarcomas
Bibliographic record
Abstract
Sarcomas are the most common extracranial solid tumours in children and have poor prognoses due to resistance to conventional treatments. Magnetic resonance-guided high-intensity focused ultrasound (HIFU) can non-invasively administer localized hyperthermia which can improve the efficacy of chemotherapy, radiation, and immunotherapy in various cancers. One of the challenges preventing the widespread clinical adoption of MRgHIFU is that thermometry is prone to motion artifacts that corrupt data. The objective of this work is to optimize MRgHIFU-controlled hyperthermia by implementing motion compensation and adaptive tumour targeting algorithms. During targeted drug delivery in a murine sarcoma model, motion artifacts resulted in underheating of the tumour, leading to less drug being released. In preparation for translation, a motion compensation algorithm demonstrated powerful artifact removal during robot-controlled motion in a phantom on a clinical MRgHIFU system. An adaptive targeting algorithm was implemented to correct a mistargeted HIFU focus with high accuracy and precision in gelatin phantoms.
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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".