WE‐D‐L100E‐01: Cone Beam CT Workshop
Bibliographic record
Abstract
The development of practical large field‐of‐view flat panel x‐ray detectors has lead to an explosion in cone‐beam computed tomography (CT) applications. In this workshop, a number of different clinical applications for cone‐beam CT will be described. Methodologies for addressing issues such as x‐ray scatter, detector lag, and limited field‐of‐view or angular coverage will also be discussed. C‐arm fluoroscopic systems have been used for image guidance in the operating room for many years. With the advent of flat panel‐based C‐arm systems, the availability of portable, operating room‐based computed tomography systems exists. The three dimensional characteristics of CT will likely improve surgical outcomes involving surgical device implantation. The development of a portable C‐arm‐based CT system, for use in the operating theater as well as in other clinical settings, will be discussed. Angiographic suites are designed to optimize vascular imaging geometry. Flat panel‐based angiography suites have undergone modifications to allow CT imaging for some specific vascular imaging applications. These systems are also capable of performance in orthopedic applications. The use of such systems in a number of clinical applications will be described, and corrections methods for x‐ray scatter and detector lag will also be presented. The use of image guidance in radiation therapy applications has skyrocketed, and in many clinical radiation therapy systems, system‐mounted cone‐beam CT systems are available for use for guiding RT delivery. The use of cone‐beam CT for radiation therapy applications will be described, and some of the challenges and limitations of this technology will be discussed. Overall, a sampling of cone‐beam CT applications will be presented in this workshop. The overall flexibility of cone‐beam CT systems to accommodate the wide range of applications described is a testimony to the long term clinical potential of cone‐beam CT systems. This workshop will also describe some of the compromises and limitations which are required when cone‐beam CT geometry is utilized.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.214 | 0.169 |
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".