Improvement of Spatial Resolution With Iterative PET Reconstruction Using Ultrafast TOF
Bibliographic record
Abstract
The impact of Time-of-Flight (TOF) on positron emission tomography (PET) spatial resolution is generally considered negligible. In this work, a two-step approach based on simulations of two-dimensional scanner configurations is taken to show that ultra-fast TOF has the potential to overcome the limitation induced by the physical size of detectors on spatial resolution. An estimation of the lower bound on spatial resolution using point sources is provided, followed by a qualitative assessment of the resolution obtained using a Hot Spot phantom. The impact of detector width, TOF resolution and TOF binning on the achieved spatial resolution is also studied. While gain beyond the expected blur due to detector size is demonstrated, the detector size remains one limiting factor albeit less prominent. The dependence on acquisition statistics to reach the full potential of TOF-induced gain in spatial resolution is demonstrated. A simulated brain phantom acquired with a fictive three-dimensional PET scanner was qualitatively analyzed and structures smaller than the typical limit are clearly made visible by reconstructing the images with a ∼13-ps TOF resolution. A potential application of this feature of ultra-fast TOF would be the design of clinical PET scanners achieving spatial resolution beyond the current state-of-the-art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".