Predicting high‐intensity focused ultrasound thalamotomy lesions using 2D magnetic resonance thermometry and 3D Gaussian modeling
Bibliographic record
Abstract
Purpose To develop a method of using two‐dimensional (2D) magnetic resonance thermometry, and three‐dimensional (3D) Gaussian modeling to predict the volume, shape, and location of 1 day postoperative T1w high‐intensity focused ultrasound lesions in medication refractory tremor patients; thereby facilitating a better comprehension of thermal damage thresholds, which can be utilized to reduce adverse events, and improve patient outcome. Methods Fifteen patients underwent magnetic resonance‐guided focused ultrasound (MRgFUS) thalamotomy, which was performed at our center using an InSightec ExAblate 4000 system (Haifa, Israel), and guided by magnetic resonance imaging using a 3 T Discovery 750 (General Electric Healthcare, Waukesha, WI, USA). For treatment monitoring, 2D MR thermometry (temperature sensitivity: −0.00909 ppm/°C, bandwidth: 279 Hz/pixel) was performed in multiple orthogonal planes (sagittal, coronal, and axial) intraoperatively. These images were temporally filtered using a general linear model approach to reduce noise. Temporal volumes of filtered temperature maps with a peak temperature 47°C were aligned and fitted with a 3D Gaussian to create a canonical heating model. We then fitted the filtered 2D temperature maps with a 3D Gaussian, and used the relationships derived from the 3D heating model to estimate the 3D temperature distribution. These temperature distributions were converted into thermal dose distributions and accumulated across time to create an accumulated thermal dose (ATD) profile. Thresholded ATD profiles were then correlated with manually traced T1‐weighted 1 day postoperative lesion volumes across patients, and linear regression slopes were plotted against varying ATD thresholds. Additionally, the Dice–Sørensen coefficient (DSC) was calculated to quantify the volumetric overlap between predicted, and actual lesions. Results On average, 18.1 (standard deviation (SD): ±4.6, range: 10–29) sonications were performed with an average peak temperature achieved of 62.4°C (SD: ±2.4, range: 58.2–67.7). An ATD threshold of 35.8 CEM43 was found to give a unity linear regression slope; this corresponded to an average DSC of 0.689 (SD: ±0.090, range: 0.476–0.815). Conclusions Using multiplanar 2D MR thermometry and 3D Gaussian modeling, we were able to achieve very good (DSC = 0.689) predictions of T1w 1 day postoperative lesion volume, shape and location at an ATD threshold of approximately 36 CEM43. Furthermore, this method has the potential to be used in clinical evaluations to further elucidate the relationship between thermal damage and clinical outcome. Accurate 3D lesion prediction will facilitate improved clinical decision making in future MRgFUS thalamotomies.
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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 | 0.000 | 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".