SU‐GG‐J‐106: Improved Planar and Megavoltage Cone‐Beam Imaging Using Low‐Z Linear Accelerator Targets
Bibliographic record
Abstract
Purpose: to examine the effect of using low atomic number (Z) target materials on megavoltage portal and cone‐beam CT (CBCT) image quality. Method and materials: for experimental measurements, four low‐Z targets were installed in a linear accelerator carousel (Varian 2100EX) for the generation of experimental spectra for imaging. The targets were composed of beryllium or aluminum with thicknesses set to approximately 60% of the CSDA range of either 4 MeV or 6 MeV electrons. For CBCT acquisition, the beam and detector were fixed, and a rotation stage was controlled by software to acquire multiple angular projections of phantoms. To examine photon energy spectra and to provide a means for optimizing the imaging system, the beam generation and detector panel were modeled using BEAMnrc/DOSXYZ Monte Carlo package. For 6MV, the beam/detector model was validated by comparing experimental and MC‐generated images of open fields. Results: The modeled, experimental spectra demonstrate the recovery of photons below 150 keV. For the 4 MeV/Be beam, for example, approximately 1/3rd of photons have energies below 60 keV. MC models of planar imaging show significant improvement of image contrast compared to the standard 6MV beam, and suggest that i) of the four energy/target combinations studied, the 4 MeV/Be combination provides the greatest contrast improvement and ii) a modest additional increase in contrast is achieved by removing the copper buildup layer from the detector. Initial low‐Z target CBCT images show improved image contrast; full quantitative results will be presented. Conclusion: The use of megavoltage electron beams combined with low‐Z targets offers the potential for improved image quality, particularly in terms of image contrast. This approach may be promising for improved and highly‐integrated on‐board megavoltage imaging. Conflict of interest: This research has been sponsored by Varian Medical Systems, Incorporated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".