Megavoltage planar and cone‐beam imaging with low‐ targets: Dependence of image quality improvement on beam energy and patient separation
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to investigate the improvement of megavoltage planar and cone-beam CT (CBCT) image quality with the use of low atomic number (Z) external targets in the linear accelerator. METHODS: In this investigation, two experimental megavoltage imaging beams were generated by using either 3.5 or 7.0 MeV electrons incident on aluminum targets installed above the level of the carousel in a linear accelerator (2100EX, Varian Medical, Inc., Palo Alto, CA). Images were acquired using an amorphous silicon detector panel. Contrast-to-noise ratio (CNR) in planar and CBCT images was measured as a function of dose and a comparison was made between the imaging beams and the standard 6 MV therapy beam. Phantoms of variable diameter were used to examine the loss of contrast due to beam hardening. Porcine imaging was conducted to examine qualitatively the advantages of the low-Z target approach in CBCT. RESULTS: In CBCT imaging CNR increases by factors as high as 2.4 and 4.3 for the 7.0 and 3.5 MeV/Al beams, respectively, compared to images acquired with 6 MV. Similar factors of improvement are observed in planar imaging. For the imaging beams, beam hardening causes a significant loss of the contrast advantage with increasing phantom diameter; however, for the 3.5 MeV/Al beam and a phantom diameter of 25 cm, a contrast advantage remains, with increases of contrast by factors of 1.5 and 3.4 over 6 MV for bone and lung inhale regions, respectively. The spatial resolution is improved slightly in CBCT images for the imaging beams. CBCT images of a porcine cranium demonstrate qualitatively the advantages of the low-Z target approach, showing greater contrast between tissues and improved visibility of fine detail. CONCLUSIONS: The use of low-Z external targets in the linear accelerator improves megavoltage planar and CBCT image quality significantly. CNR may be increased by a factor of 4 or greater. Improvement of the spatial resolution is also apparent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".