WE‐G‐BRC‐02: A Portable Optical CT Scanner for Interactive Teaching of Imaging Principles
Bibliographic record
Abstract
Purpose: To develop a portable scanner that demonstrates the principles of radiography and computed tomography (CT). Methods: The traditional methods for teaching the physics of medical imaging rely on lectures, followed by demonstrations on a clinical system that is not easily accessible or programmed for educational purposes. We have developed a scaled‐down portable CT imaging system suitable for interactive “real time” demonstrations using a laptop computer during a classroom or lab session. Our optical system uses light rays in lieu of x‐rays so that experiments can be conducted while posing no electrical or radiation hazards to instructors and students. The desk‐top CT imaging device will be supplied with a learning kit of experimental test phantoms, lab manuals, instructional videos, and specialized software that demonstrate 2D radiographic and 3D CT image reconstruction methods. These approaches are relevant to imaging systems used in digital diagnostic imaging and image‐guided therapy. Results: A low‐cost portable system (< $15,000) has been manufactured (http://www.deskcat.com/) to enrich the studentˈs learning experience and improve the retention of fundamental imaging concepts. Students learn about spatial resolution, contrast resolution, system linearity, image artifacts and they perform quantitative measurements in 3D space, using image visualization software tools and specialized test phantoms (7.2 cm diameter × 5.3 cm long). Early reaction from instructors and students alike has been very encouraging. Conclusions: This learning package should prove attractive to universities with medical physics programs in Departments of Physics, Medical Biophysics, or Biomedical Engineering, as well as Medical Schools with residency training programs (Medical Imaging, Radiation Oncology). The systemˈs modular nature allows extensions for future coverage of related topics such as nuclear and molecular SPECT imaging. Through future developments, it may also be possible to model the dose deposition patterns from intensity‐modulated radiotherapy beams using ultraviolet exposure of radiochromic gel volumes. This research was sponsored by the Ministry of Research and Innovation, Government of Ontario, Canada (ORDCF Grant, OCITS Project) and by Modus Medical Devices as the industry partner. We also thank The University of Western Ontario for providing funding through its “Fellowship for Teaching Innovation”. This supported one of the authors (RT) during a summer studentship.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".