Intensity-modulated fluence patterns for task-specific imaging in cone-beam CT
Bibliographic record
Abstract
Cone-beam CT images of a patient with a pre-defined distribution of noise in the image and dose to the patient can be accomplished through the development of advanced compensation schemes. Such compensation schemes involve delivery of x-ray fluence patterns that vary in intensity both across a single projection image (u,v) and for different projection view angles (θ) and provide the ability to perform intensity-modulated cone-beam CT. Implementation of an intensity-modulated cone-beam CT system for task-specific imaging has potential for tremendous reductions in patient dose and x-ray scatter reaching the detector. Pursuing this advanced imaging technique requires detailed characterization of the cone-beam CT platform. Determination of appropriately modulated fluence patterns relies on knowledge of numerous properties of the imaging system, including the constraints imposed by the modulator, the magnitude of x-ray scatter under different patient sizes and modulator positions, and properties of the detector. With an estimate of the patient anatomy and knowledge of the imaging system, an iterative process can be used to determine modulated fluence patterns corresponding to an image prescribed for the specific task and patient. Delivery of such modulated fluence patterns provide a CBCT image tailored to a specific patient and imaging task offering the optimum balance between image quality and patient dose. Specifically, arbitrary regions of interest requiring high image signal-to-noise ratio can be generated through knowledgeable spatio-angular intensity modulation, allowing image quality to degrade in other regions in order to minimize x-ray scatter and imaging dose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".