Preuve de concept in vitro de la navigation par résonance magnétique en conditions physiologiquement réalistes
Bibliographic record
Abstract
This research aims to demonstrate the feasibility of magnetic resonance navigation (MRN) as a treatment for unresectable, malignant liver tumors. This method consists in steering magnetic drug-eluting microbeads in hepatic arteries towards diseased liver lobes using an unmodified magnetic resonance imaging (MRI) scanner, with regards to physiological, anatomical and technological limitations. To reproduce a physiological flow during the MRN experiment, the arterial flow was measured using phase contrast MRI on seven porcine models. In addition, to evaluate the feasibility of creating in vivo flow conditions that are suitable for MRN, a balloon catheter positioned in the proper hepatic artery was used simultaneously with a 0.5 mL/s saline infusion to reduce mean flow velocity and systolic-diastolic variation. In parallel, to characterize the arterial flow profile as a function of embolization level for treatment planning, a quantitative digitally subtracted angiography (qDSA) program was developed and used to measure arterial flow rate in individual vessel segments of a realistic porcine hepatic arterial phantom under fluoroscopy with injection of contrast agent at 0.25 mL/s. Then, a proof-of-concept in vitro MRN experiment was performed under flow rate conditions similar to those measured in vivo to demonstrate the feasibility of using a 20 mT/m gradient sequence in an unmodified MRI scanner to selectively steer aggregates composed of 20 magnetic microbeads in a one-bifurcation hepatic arterial phantom. Phase contrast imaging on pigs revealed that using a balloon catheter decreases the mean hepatic arterial flow velocity from 20.5 ± 15.3 cm/s to 8.4 ± 5.0 cm/s (uncertainty representing the standard deviation) as well as the systolic-diastolic variation from 24 ± 14 cm/s to 4.9 ± 2.3 cm/s, which is compatible with MRN. The qDSA program provided a flow estimate in individual segments with a root-mean-square error of 34%, but further development is needed to allow the characterization of the arterial flow profile as a function of embolization level. For the MRN experiment, the steering efficiency obtained was 100%. This research shows that the use of a balloon catheter reduces arterial hepatic flow velocity and variation and that qDSA is a promising flow measurement method in the context of transarterial chemoembolization. In addition, this research shows that MRN is possible under realistic physiological and anatomical settings, with reduced flow and with an unmodified MRI scanner.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".