TU‐E‐103‐01: Image Quality Models in Advanced CT Applications
Bibliographic record
Abstract
The last decade saw the development of new x‐ray tomographic imaging technologies, such as flat‐panel detector cone‐beam CT and tomosynthesis, now prevalent in applications ranging from diagnostic imaging to image‐guided interventions. Such technologies proceeded in stride with models of imaging performance developed to provide a rigorous understanding of the factors governing image quality and help accelerate system design and translation. The decade ahead promises important advances ‐ for example: statistical and iterative model‐based image reconstruction; dual‐energy and spectral tomography; photon counting detectors; phase contrast tomography; and understanding the performance of model and real observers in the context of volumetric data. The theoretical models of imaging performance now developing alongside such advanced technologies are the topic of this symposium. Dr. Nishikawa will introduce the broad and challenging landscape of such technologies and applications. Dr. Siewerdsen will discuss image quality models for dual‐energy CT and the extension from conventional filtered backprojection to statistical / iterative reconstruction methods. Dr. Cunningham will describe the development of cascaded systems analysis for new photon counting detector systems. Dr. Chen will demonstrate how image quality models and performance measurement in x‐ray (absorption) CT can be extended to differential phase‐contrast CT. Finally, Dr. Bochud will address the performance of observers in volumetric data, highlighting newly appreciated factors that are distinct from conventional models and understanding in the context of 2D (or single slice) image interpretation. Learning Objectivess: 1. Understand the growing landscape of advanced tomographic imaging technologies and applications. 2. Understand how image quality models developed over the last decade for CT, cone‐beam CT, and tomosynthesis can be extended to: a.) dual‐energy / spectral CT b.) statistical and iterative image reconstruction c.) photon counting detectors d.) differential phase contrast CT 3. Understand the distinctions and new considerations in observer performance and image quality assessment in volumetric data.. National Institutes of Health. Carestream Health. Siemens Healthcare.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".