The impact of long axial field of view (LAFOV) PET on oncologic imaging
Bibliographic record
Abstract
The development of long axial field of view (LAFOV) positron emission tomography coupled with computed tomography (PET/CT) scanners might be considered the biggest step forward in PET imaging since it became a mainstream clinical modality. Despite increased capital and maintenance costs and data storage requirements, the improvement in image quality, significantly faster acquisition times and lower radiopharmaceutical administered activities, allow a high quality and more efficient clinical service. This step change in technology overcomes some of the limitations of standard short axial field of view scanners. It allows simultaneous imaging of all body systems, and with the ability to obtain high temporal resolution data, it increases potential research applications, particularly in multisystem disease or for dosimetry measurements of novel radiopharmaceuticals. The improvements in sensitivity and signal-to-noise facilitates the use of tracers with long half-lives and low administered activity (e.g. [ 89 Zr]-labelled monoclonal antibodies) or very short half-lives (e.g. [ 82 Rb]), opening up applications that hitherto have been challenging. It is early in the evolution of LAFOV PET/CT and the advantages these systems offer have still to be fully realised in providing additional impact in clinical practice. In this article we describe the potential advantages of LAFOV PET technology and some of the clinical and research applications where it has been applied as well as some of the future developments that may enhance the modality further.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".