A hybrid approach for rapid, accurate, and direct kilovoltage radiation dose calculations in CT voxel space
Bibliographic record
Abstract
PURPOSE: To develop and validate a fast and accurate method that uses computed tomography (CT) voxel data to estimate absorbed radiation dose at a point of interest (POI) or series of POIs from a kilovoltage (kV) imaging procedure. METHODS: The authors developed an approach that computes absorbed radiation dose at a POI by numerically evaluating the linear Boltzmann transport equation (LBTE) using a combination of deterministic and Monte Carlo (MC) techniques. This hybrid approach accounts for material heterogeneity with a level of accuracy comparable to the general MC algorithms. Also, the dose at a POI is computed within seconds using the Intel Core i7 CPU 920 2.67 GHz quad core architecture, and the calculations are performed using CT voxel data, making it flexible and feasible for clinical applications. To validate the method, the authors constructed and acquired a CT scan of a heterogeneous block phantom consisting of a succession of slab densities: Tissue (1.29 cm), bone (2.42 cm), lung (4.84 cm), bone (1.37 cm), and tissue (4.84 cm). Using the hybrid transport method, the authors computed the absorbed doses at a set of points along the central axis and x direction of the phantom for an isotropic 125 kVp photon spectral point source located along the central axis 92.7 cm above the phantom surface. The accuracy of the results was compared to those computed with MCNP, which was cross-validated with EGSnrc, and served as the benchmark for validation. RESULTS: The error in the depth dose ranged from -1.45% to +1.39% with a mean and standard deviation of -0.12% and 0.66%, respectively. The error in the x profile ranged from -1.3% to +0.9%, with standard deviations of -0.3% and 0.5%, respectively. The number of photons required to achieve these results was 1 x 10(6). CONCLUSIONS: The voxel-based hybrid method evaluates the LBTE rapidly and accurately to estimate the absorbed x-ray dose at any POI or series of POIs from a kV imaging procedure.
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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".