Po‐Poster ‐ 20: Octree based compression method of DICOM images for voxel number reduction and faster Monte Carlo simulations
Bibliographic record
Abstract
Purpose: Diminish the number of voxels created from a set of DICOM images while keeping critical information at tissue interfaces needed in a Monte Carlo simulation without compromising the physical quality of the voxelized image. An algorithm was developed to apply an octree compression to DICOM images. The algorithm works as follows: the whole set of DICOM image is assumed as a cube. It is then split in eight equal smaller cubes. Each of the cubes is checked for density homogeneity. If a high density gradient is encountered in a cube, it is also split in eight equal parts. This process goes on until the minimum specified voxel size is reach. The resulting image is composed of various voxels size. To verify precision, Monte Carlo simulation with GEANT4 using a narrow beam passing through several high density gradients was done. The resulting number of voxels range from 5 to 20% of the original size depending on configuration. The voxel area on a typical slice is 1 square voxel (i.e. no compression) at high density gradient to 64 square voxel for homogeneous sections. Mean volume was about 5.43 voxels for the phantom used. Monte Carlo simulation shows less then 1% difference in dose at the high gradient interface. The octree compression is an excellent method to compress DICOM information for Monte Carlo treatment planning without losing precision at the interfaces in homogeneous regions. Given good parameters the algorithm also has the ability to smooth digital noise in the homogenous area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".