Multi-Angle Image Reconstruction Method for a Planar Organ-Targeted PET Detector
Bibliographic record
Abstract
Organ-targeted PET detectors with a pair of planar detectors optimized to sustain a large solid angle around an organ of interest offer versatility in imaging applications not achievable with ring detectors. However, this versatility comes with a trade-off of a lower axial spatial resolution compared to the transaxial spatial resolution, which hinders the ability to perform certain image processing tasks such as multi-modal image fusion, image segmentation, and feature extraction. To address these limitations, a method was developed for generating a higher axial spatial resolution composite image by fusing images acquired from multiple angles. Here we present a workflow of a multi-angle maximum likelihood expectation maximization (MLEM) algorithm, which reconstructs multiple datasets acquired from different angles using two planar detector heads. To demonstrate the feasibility of this method, acquisitions were performed using a planar organ-targeted PET detector oriented at 0° and 90°, and composite images were reconstructed using the multi-angle MLEM on (1) multi-angle datasets from four spheres (ranging in size from 12.4 mm, 7.9 mm, 6.2 mm, 5 mm) and (2) a NEMA NU4-2008 image quality (IQ) phantom filled with [¹⁸F]Fluorodeoxyglucose. The composite images indicate qualitative improvement in overall image quality and quantitative improvement in spatial resolution. For example, in the composite images of the 12.4 mm sphere, the Full-Width Half-Maximum (FWHM) of the line profiles along the axial plane was improved by ~5.63 mm. Also, composite images from the IQ phantom demonstrate more accurate representations of the hot rods and cold chambers along the axial plane vs. images reconstructed without fusion. The presented multi-angle reconstruction may provide a practical means to improve the 3D images reconstructed using data from planar organ-targeted PET detectors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".