The conversion of electroanatomic maps for compatibility with treatment planning systems in cardiac radioablation target volume definition
Bibliographic record
Abstract
BACKGROUND: Cardiac radioablation (CRA) is a new and promising treatment modality for patients with ventricular tachycardia refractory to standard-of-care treatment. Electroanatomic maps are used to define radiation target volumes; however, there is currently no native method to import electroanatomic maps into the treatment planning system (TPS). PURPOSE: To develop Edico, a semi-automated tool to enable electroanatomic map import into a TPS, by converting electroanatomic maps to a Digital Imaging and COmmunications in Medicine (DICOM) standard. The overall aim is to facilitate target volume delineation and improve workflow efficiency in treating patients. METHODS: Edico imports voltage and spatial data from electroanatomic maps and sorts these into voxels to be exported in a DICOM format, with each voxel containing the average voltage value of the data that falls within it. Three different rectangular electroanatomic maps were created and processed using Edico to ensure that expected features are maintained through processing. A sensitivity analysis of voxel size was completed using 19 different electroanatomic maps processed at five different sets of voxel dimensions, for a total of 95 resulting voxelized datasets. The coefficient of variation in each populated voxel in the datasets was analyzed to determine which voxel sizes are necessary to ensure that data loss is kept to a minimum throughout processing, despite averaging. Five electroanatomic maps were used to re-contour clinical target volumes and planning target volumes for previously-treated patients with their electroanatomic maps now directly registered to their planning computed tomography (CT) scans. RESULTS: All three rectangular test electroanatomic maps were processed as expected. All tested voxel sizes resulted in low coefficients of variation overall, with the exception of the largest voxel size of 1.8 × 1.8 × 8 mm. When using Edico, a user should choose voxel dimensions similar to or smaller than those of a planning CT. Of five pairs of clinical and planning target volumes from previously treated patients, adjustments were made to four (80%), retrospectively, using the electroanatomic maps generated using Edico, registered to the patients' planning CTs. CONCLUSIONS: Edico provides a reliable solution for electroanatomic map import into a TPS and facilitates clinical and planning target volume identification in CRA.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".