WE‐C‐103‐02: BEST IN PHYSICS (IMAGING) ‐ Strategies for Fluence Field Modulated CT
Bibliographic record
Abstract
Purpose: Fluence field modulated computed tomography (FFMCT) proposes using dynamically changing fluence fields during image acquisition for advanced dose reduction and image quality optimization. This work compares the relative effectiveness of FFMCT when the modulator is subject to limiting constraints, such as might be introduced by real physical modulators. Methods: Several tasks were defined for a simulated anthropomorphic chest phantom by defining different regions of high and low image quality, characterized by the signal standard deviation. Optimal modulation fluence patterns were sought for each task using a simulated annealing optimization script, which attempts to achieve the image quality plan under a global dosimetric constraint. Optimization was repeated under different types of modulation constraints representing several different realistic modulator designs (e.g. dynamically varying apertures, discrete fixed apertures, or shaped static filters). Results were compared based on predicted dose outcomes and agreement with the prescribed image quality criteria. Results: Compared to static modulators (e.g. bowtie filter), fluence field modulation approaches utilizing dynamically changing modulators showed improved agreement with the prescribed outcomes, including greater uniformity of image noise over the target regions of interest. For some tasks, highly constrained modulators approached outcomes similar to that of the ideal unconstrained case. Average integral dose reduction compared to a uniform reference field exceeded 20% for all modulation methods. Conclusions: The results support that FFMCT can achieve regionally varying image quality distributions in good agreement with user‐prescribed values, while reducing dose to the patient. The outcomes also support that the benefits of fluence field modulation may be yielded even when highly constrained modulators are used, suggesting practical, feasible implementations of FFMCT may be possible. Research funded in part by the Ontario Graduate Scholarships (OGS), Natural Sciences and Engineering Research Council of Canada (NSERC), Elekta Inc. and the Ontario Research Fund (ORF)
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".