Multi‐angle acquisition and 3D composite reconstruction for organ‐targeted PET using planar detectors
Bibliographic record
Abstract
BACKGROUND: This study investigates a multi-angle acquisition method aimed at improving image quality in organ-targeted PET detectors with planar detector heads. Organ-targeted PET technologies have emerged to address limitations of conventional whole-body PET/CT systems, such as restricted axial field-of-view (AFOV), limited spatial resolution, and high radiation exposure associated with PET procedures. The AFOV in organ-targeted PET can be adjusted to the organ of interest, minimizing unwanted signals from other parts of the body, thus improving signal collection efficiency and reducing the dose of administered radiotracer. However, while planar detector PET technology allows for quasi-3D image reconstruction due to the separation between detector heads, it suffers from degraded axial spatial resolution and, consequently, reduced recovery coefficients (RCs) along the axial direction perpendicular to the detectors. PURPOSE: The purpose of this study was to evaluate the concept of multi-angle image acquisition with two planar PET detectors and composite full 3D image reconstruction. This leverages data collection from multiple polar angles to improve the axial spatial resolution in the direction perpendicular to the detector heads. In such, the concept allows to overcome the intrinsic limitations of planar detectors in axial resolution. METHODS: This study evaluates the improvement in the quality of images acquired with the Radialis organ-targeted PET camera through multi-angle image acquisition, in both experimental and simulated imaging scenarios. This includes the use of custom-made phantom with fillable spherical hot inserts, the NEMA NU4-2008 image quality (IQ) phantom, and simulations with a digital brain phantom. The analysis involves the comparison of line profiles drawn through the spherical hot inserts, image uniformity, RCs, and the reduction of smearing observed in the axial planes with and without the multi-angle acquisition strategy. RESULTS: Significant improvements were observed in reducing smearing, enhancing image uniformity, and increasing RCs using the evaluated multi-angle acquisition method. In the composite images, the hot spheres appear more symmetrical in all planes. The image uniformity, calculated from the IQ phantom, improves from 7.79% and 10.98%, as measured in the images from the individual acquisitions, to 2.72% in the composite image. There is also an overall improvement in the RCs as measured from the hot rods of the IQ phantom. Furthermore, the simulation study using the digital human brain phantom demonstrates minimal smearing in the four-angle scan, as opposed to a two-angle scan. CONCLUSION: The multi-angle acquisition method offers a promising approach to transform planar PET detector technology into a true tomographic organ-targeted PET system and to enable improvement in image quality while preserving a versatility inherent to planar detector technology. Future research will focus on optimizing the multi-angle imaging protocol, including adjustments to detector separations, number of acquisition angles, and reconstruction iterations, alongside incorporating TOF, and reconstruction with point spread function modeling to further improve image quality.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".