In vivo optimisation study for multi-baseline MR-based thermometry in the context of hyperthermia using MR-guided high intensity focused ultrasound for head and neck applications
Bibliographic record
Abstract
PURPOSE: In this in vivo study, the feasibility to perform hyperthermia treatments in the head and neck using magnetic resonance image-guided high intensity focused ultrasound (MRgHIFU) was established using a porcine acute model. MATERIALS AND METHODS: Porcine specimens with a weight between 17 and 18 kg were treated in the omohyoid muscle in the neck. Hyperthermia was applied with a target temperature of 41 °C for 30 min using a Sonalleve MRgHIFU system. MR-based thermometry was calculated using water-proton resonance frequency shift and multi-baseline look-up tables indexed by peak-to-peak displacement (Dpp) measurements using a pencil-beam navigator. Three hyperthermia experiments were conducted at different Dpp values of 0.2, 1.0 and 3.0 mm. An optimisation study was carried out to establish the optimal parameters controlling the multi-baseline method that ensured a minimisation of spatial-average peak-to-peak temperature (TSA-pp) and temperature direct current bias (TSA-DC). RESULTS: The multi-baseline technique reduced considerably the noise on both TSA-pp and TSA-DC. The reduction of noise was more important when Dpp was higher. For Dpp = 3 mm the average (±standard deviation (SD)) of TSA-pp and TSA-DC was reduced from 4.5 (± 2.5) and 2.5 (±0.6) °C, respectively, to 0.8 (± 0.7) and 0.09 (± 0.2) °C. CONCLUSIONS: This in vivo study showed the level of noise in PRFS-based thermometry introduced by respiratory motion in the context of MRgHIFU hyperthermia treatment for head and neck and the feasibility of reducing this noise using a multi-baseline technique.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".