Optimizing the Multi-Angle Imaging Method for Planar Organ-Targeted PET Detectors
Bibliographic record
Abstract
Organ-targeted Positron Emission Tomography (PET) has demonstrated the potential to overcome limitations of conventional whole-body (WB) PET/CT systems, such as restricted axial field-of-view (AFOV), limited spatial resolution, and high radiation exposure. The AFOV in organ-targeted PET with planar detector heads can be adjusted to the organ of interest, minimizing unwanted signals from elsewhere in the body, improving signal collection efficiency, and reducing the dose of radiotracer administered. However, while planar detector PET technology allows for quasi-3D image reconstruction due to the separation between detector heads, a limited angular view degrades axial spatial resolution, affecting recovery coefficients (RCs) and causing object smearing in reconstructed images along the axial direction perpendicular to the detectors’ plane. Previously, we introduced a multi-angle imaging method to improve the quality of images acquired with a planar organ-targeted PET camera. Here, we determine the optimal number of acquisition angles and image reconstruction iterations suitable for brain imaging with a wide detector separation. For quantitative analysis, we use the NEMA NU4 2008 standard image quality phantom. Our results indicate that performing a 4 -angle scan, with 45° increment rotations of the detector heads, is an optimal detection scheme. Specifically, it achieves an image uniformity of 6.29%, recovery coefficients of 0.15,0.33,0.52, and 0.73 for the 2-, 3-, 4, and $5-\mathrm{mm}$ rods, and spill-over ratios of 0.23 and 0.18 for the air and water chambers, respectively, significantly exceeding performance metrics of the standard 1 -angle image. Moreover, increasing the number of image reconstruction iterations minimizes spill-over of activity within air and water chambers. Improved image quality was obtained using the same scan duration time in the 4 -angle composite image compared to the standard 1-angle image.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".