Enhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images
Bibliographic record
Abstract
Epilepsy, a common neurological disorder causing recurring seizures. Magnetic Resonance-guided Laser Interstitial Thermal Therapy (MRgLITT) is a promising minimally-invasive technique to ablate the target especially for drug resistance epilepsy. MRgLITT employs a laser fiber to ablate brain tissue through heat deposition, offering real-time monitoring through Magnetic Resonance (MR) thermometry images and precise treatment planning using MRI planning images. In this study, we developed an AI-based approach utilizing a U-Net model, a convolutional neural network architecture widely used for image to image translation, to predict MR thermometry images from anatomical MRI planning images from a dataset of 81 patients with mesial temporal lobe epilepsy. The model’s performance was evaluated on a test dataset using the structural similarity index (SSIM) and root mean squared error (RMSE).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".