Preclinical Evaluation of an MR-guided High Intensity Focused Ultrasound Platform for in-utero Therapy
Bibliographic record
Abstract
Current fetal interventions are invasive, posing risks of premature labor and fetal mortality. Magnetic Resonance-guided High-Intensity Focused Ultrasound (MRgHIFU) offers a non-invasive, incision-free alternative, delivering targeted thermal energy under MRI guidance while avoiding damage to fetal membranes. Although clinically used for conditions like uterine fibroids and bone metastases, it is not yet approved for fetal applications.The goal of this project was to evaluate whether a Health-Canada approved MRgHIFU platform is safe and effective for the delivery of in-utero thermal treatment for twin reversed arterial perfusion sequence (TRAPS). The first aim assessed the efficacy of this modality through pre-clinical acute studies on New-Zealand white rabbits (n=6), targeting umbilical vessels within fetal liver (n=18 fetuses) for HIFU treatment to remove them from in-utero circulation. Changes in cardiac output and placental perfusion were assessed using ultrafast ultrasound imaging. Success was defined as target fetal termination, confirming an overall 83% success. Thermal ablation of the target tissue was confirmed in all experiments. The second aim evaluated safety using pre-clinical survival studies in the same model (n=10, 1 sham), targeting one fetus per rabbit and monitoring two cohorts (2-day and 5-day), thermally ablating intrafetal umbilical vessels. Rabbits exhibited normal health post-treatment, with three delivering naturally at E28-E29. Necropsy revealed an average of 83% viability among non-targeted fetuses, with no HIFU marks observed on non-target fetuses. The third aim prepared for clinical translation through retrospective analysis of 2D multi-slice MR images from pregnant patients. This established a virtual treatment planning platform, assessed fetal umbilical location relative to the skin, identified barriers such as the placenta and sensitive maternal tissue, and established inclusion/exclusion criteria for potential candidates. Overall, this thesis contributes to the advancement of non-invasive fetal interventions by validating MRgHIFU for in-utero treatment of TRAPS sequence. Through pre-clinical studies, it evaluates efficacy and safety while preparing for clinical translation by assessing anatomical feasibility and patient selection criteria. This work not only aims to improve fetal outcomes and reduce risks associated with invasive procedures but also serves as a stepping stone for applying this technology to more common and complex fetal anomalies in the future.
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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 | 0.000 |
| 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 | 0.001 | 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".