Investigation of combined <scp>kV</scp>/<scp>MV CBCT</scp> imaging with a high‐<scp>DQE MV</scp> detector
Bibliographic record
Abstract
PURPOSE: Combined kV-MV cone-beam tomography (CBCT) imaging has been proposed for two potentially important image-guided radiotherapy applications: (a) scan time reduction (STR) and (b) metal artifact reduction (MAR). However, the feasibility of these techniques has been in question due to the low detective quantum efficiencies (DQEs) of commercially available electronic portal imagers (EPIDs). The goal of the work was to test whether a prototype high DQE MV detector can be used to generate acceptable quality pretreatment CBCT images at acceptable dose levels. METHODS: 6MV and 100 kVp projection data were acquired on a Truebeam system (Varian, Palo Alto, CA). The MV data were acquired using a prototype EPID containing two scintillators (a) a standard copper-gadolinium oxysulfide (Cu-GOS) screen having a zero-frequency DQE (DQE(0)) value of 1.4%, and (b) a prototype-focused cadmium tungstate (CWO) pixelated "strip" with a DQE(0) = 22%. The kV data were acquired using the standard onboard imager (DQE(0) = 70%). The angular spacing of the MV projections was 0.81° and the source output was 0.03 MU/projection while the kV projections were acquired with an angular spacing of 0.4° at 0.3 mAs/projection. Image quality was evaluated using (a) an 18-cm diameter electron density phantom (CIRS, Norfolk, VA) with nine contrast inserts and (b) the resolution section of the 20-cm diameter Catphan phantom (The Phantom Laboratory, Greenwich, NY). For the MAR studies, two opposing CIRS phantom inserts were replaced by steel rods. The reconstruction methods were based on combining MV and kV data into one sinogram. The MAR reconstruction utilized mostly kV raw data with only those rays corrupted by metal requiring replacement with MV data (total absorbed dose = 0.7 cGy). For the STR study, projections from partially overlapping 105°kV and MV acquisitions were combined to create a complete dataset that could have been acquired in 18 sec (absorbed dose = 2.5 cGy). MV-only (4.3 cGy) and kV-only (0.3 cGy) images were also reconstructed. RESULTS: The average signal-to-noise ratio (SNR) of the inserts in the MV-only CWO and GOS CIRS phantom images were 0.62× and 0.12× the SNR of the inserts in kV-only image, respectively. The limiting spatial resolutions in the MV-only GOS, MV-only CWO, and kV-only Catphan images were 3, 6, and 8 lp/cm, respectively. In the combined kV/CWO STR reconstruction, all contrast inserts were visible while only two were detectable in the kV/Cu-GOS image due to high levels of noise (average SNRs of kV/CWO and kV/GOS inserts were 0.97× and 0.18× the SNR of the kV-only inserts, respectively). In the kV-MV MAR reconstructions, streaking artifacts were substantially reduced with all inserts becoming clearly visible in the kV/CWO image while only two were visible in the kV/Cu-GOS image (average SNRs of the kV/CWO and kV/Cu-GOS CIRS with metal inserts were 0.94× and 0.35× the SNRs of the kV-only CIRS without metal inserts). CONCLUSIONS: We have demonstrated that a high-DQE MV detector can be applied to generating high-quality combined kV-MV images for SRT and MAR. Clinically acceptable doses were utilized.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".