Investigation of calibration-based projection domain dual energy decomposition CBCT technique for brain radiotherapy applications
Bibliographic record
Abstract
The purpose of the present study was to develop and evaluate a practical dual-energy imaging approach for enhancing on-board cone-beam CT (CBCT) image quality for brain radiotherapy applications. The proposed primary technique involves a projection domain calibration procedure. In-house fabricated aluminum and acrylic step wedges were stacked and oriented orthogonally to each other to produce 72 unique combinations of two-material path lengths, i.e. 8 acrylic steps × 9 aluminum steps. High (120 kV) and low (70 kV) energy projections were acquired of the step wedges and a 3rd order polynomial fit was used to map the log-normalized projection intensities to the known acrylic and aluminum thicknesses. The subsequent model was tested on two phantoms: 1) in-house DE phantom with PMMA background and calcium inserts of different concentrations (5 mg/mL, 200 mg/mL and 400 mg/mL) and 2) a RANDO head phantom. The decomposed projections were reconstructed separately as aluminum-only and acrylic-only reconstructions. In addition, virtual monochromatic projections (VMPs) obtained by combining the aluminum-only and acrylic-only projections were reconstructed at different keVs. A quantitative improvement was observed in the SDNR (signal difference to noise ratios) of the calcium inserts using Aluminum-reconstructions and synthesized VMPs (40 to 100 keV) compared to the single energy reconstructions. A reduction in beam hardening was observed as well. In addition, a qualitative improvement in soft-tissue visualization was observed with the RANDO phantom reconstructions. The findings indicate the potential of dual energy CBCT images: material specific images as well as VMPs for improved CBCT-based image guidance. The present approach can readily be applied on existing commercial systems and a feasibility study on patients is a worthwhile investigation.
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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 | 0.001 | 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 | 0.001 |
| 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".