Towards cone-beam CT thermometry
Bibliographic record
Abstract
Temperature monitoring and therefore the final treatment zone achieved during a cone-beam CT (CBCT) guided ablation can prevent overtreatment and undertreatment. A novel method is proposed to detect changes in consecutive CBCT images obtained from projection reconstructions during an ablation procedure. The possibility is explored of using this method to generate thermometry maps from CBCT images, which can be used as an input function for ablation treatment planning. This novel method uses a baseline and an intermittent CBCT scan, which are routinely acquired to confirm the needle position and monitor progress of the ablation. Accurate registration is required and assumed in vitro and ex vivo. A Wronskian change detector algorithm is applied on the compensated images to obtain a difference image between the intermittent and baseline scans. Finally, a thermal map created by applying a calibration determined experimentally is used to obtain the corresponding temperature at each pixel or voxel. We applied Wronskian change detector to detect the difference of two CBCT images, which have low signal to noise ratio, and calibrate Wronskian change model to temperature data using a gel phantom. We tested the temperature mapping with water and gel phantoms as well as pig shoulder. The experimental results show this method can detect temperature change within 5°C for a voxel size of 1mm3 (within clinical relevancy), and by consequence delineate the ablation zone. The preliminary experimental results show that CBCT thermometry is possible and promising, but may require pre-processing, such as registration for motion compensation between the baseline and intermittent scans. Further, quantitative evaluations have to be conducted for validation prior to clinical assessment and translation. CBCT is a widely available technology that could make thermometry clinically practical as an enabling component of iterative ablation treatment planning.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".