Synchrotron radiation biomedical imaging and radiotherapy: from the UK to the Antipodes
Bibliographic record
Abstract
Although the general public might think of 'X-rays' as they are applied to imaging (radiography) and for the treatment of disease (radiotherapy), the use of synchrotron radiation (SR) X-ray beams in these areas of science was a minor activity 50 years ago. The largest gains in science from SR were seen to be in those areas where signals were weakest in laboratory instruments, such as X-ray diffraction and spectroscopy. As the qualities of SR X-rays were explored and more areas of science adopted SR-based methods, this situation changed. About 30 years ago, the clinical advantages of using SR X-ray beams for radiography, radiotherapy and clinical diagnostics started to be investigated. In the UK, a multi-disciplinary group, consisting of clinicians, medical physicists and other scientists working mainly with the Synchrotron Radiation Source (SRS) in Cheshire, started to investigate techniques for diagnosis and potentially a cure for certain cancers. This preliminary work influenced the design of new facilities being constructed around the world, in particular the Imaging and Medical Beam Line on the Australian Synchrotron in Melbourne. Two authors moved from the UK to Australia to participate in this exciting venture. The following is a personal view of some of the highlights of the early-year SRS work, following through to the current activities on the new facility in Australia. This article is part of the theme issue 'Fifty years of synchrotron science: achievements and opportunities'.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".