fastCAT: Fast cone beam CT (CBCT) simulation
Bibliographic record
Abstract
Purpose To develop fastCAT, a fast cone‐beam computed tomography (CBCT) simulator. fastCAT uses pre‐calculated Monte Carlo (MC) CBCT phantom‐specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Methods Pre‐calculated x‐ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x‐ray beam spectra are simulated with EGSnrc for 2.5‐ and 6 MeV electron beams incident on a variety of target materials and kV x‐ray beam spectra are calculated analytically for an x‐ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled—a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy‐dependent scatter kernels were created in Topas for two 16 cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1–5 mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B‐100. Results fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024 1024 10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. Conclusions We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".