Development of an Image Analysis Based Treatment Planning Environment to Assess Targetability of Placental Vascular Anomalies for in-utero MRI-guided Focused Ultrasound Thermal Ablation
Bibliographic record
Abstract
Placental vascular anomalies, such as twin-to-twin transfusion syndrome (TTTS) and placentalchorioangiomas, pose significant risks to pregnancy outcomes. Magnetic resonance-guided high- intensity focused ultrasound (MRgHIFU) offers a potential non-invasive treatment for these conditions. However, current proprietary MRgHIFU treatment planning software does not support placental investigation. To address this gap, the FUSion Planner software was developed, allowing for the importation of patient MR imaging data to plan HIFU treatments and simulate ultrasound effects. FUSion Planner can model ultrasound pressure, temperature increases, and thermal dose based on patient-specific data. In a pilot study involving pregnant patient imaging data, FUSion successfully planned and simulated HIFU targeting of the placental surface, achieving focal heating with a peak temperature of 58°C and a projected lesioned area of approximately 49mm2. These findings demonstrate the software’s capabilities in MRgHIFU planning and simulation for placental therapy, enabling further investigation of MRgHIFU in maternal-fetal care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".