Fluence field optimization for noise and dose objectives in CT
Bibliographic record
Abstract
PURPOSE: Selecting the appropriate imaging technique in computed tomography (CT) inherently involves balancing the tradeoff between image quality and imaging dose. Modulation of the x-ray fluence field, laterally across the beam, and independently for each projection, may potentially meet user-prescribed, regional image quality objectives, while reducing radiation to the patient. The proposed approach, called fluence field modulated CT (FFMCT), parallels the approach commonly used in intensity-modulated radiation therapy (IMRT), except "image quality plans" replace the "dose plans" of IMRT. This work studies the potential noise and dose benefits of FFMCT via objective driven optimization of fluence fields. METHODS: Experiments were carried out in simulation. Image quality plans were defined by specifying signal-to-noise ratio (SNR) criteria for regions of interest (ROIs) in simulated cylindrical and oblong water phantoms, and an anthropomorphic phantom with bone, air, and water equivalent regions. X-ray fluence field patterns were generated using a simulated annealing optimization method that attempts to achieve the spatially-dependent prescribed SNR criteria in the phantoms while limiting dose (to the volume or subvolumes). The resulting SNR and dose distributions were analyzed and compared to results using a bowtie filtered fluence field. RESULTS: Compared to using a fixed bowtie filtered fluence, FFMCT achieved superior agreement with the target image quality objectives, and resulted in integral dose reductions ranging from 39 to 52%. Prioritizing dose constraints for specific regions of interest resulted in a preferential reduction of dose to those regions with some tradeoff in SNR, particularly where the target low dose regions overlapped with regions where high SNR was prescribed. The method appeared fairly robust under increased complexity and heterogeneity of the object structure. CONCLUSIONS: These results support that FFMCT has the potential to meet prescribed image quality objectives, while decreasing radiation exposure to the patient. Tradeoffs between SNR and dose may not be eliminated, but might be more efficiently managed using FFMCT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".