ArticleMedical physics2026
Image reconstruction and elongation artifact reduction for a dual-panel dedicated prostate PET scanner.
Article in Medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Image reconstruction and elongation artifact reduction for a dual-panel dedicated prostate PET scanner.Medical physics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThe development of PET scanners dedicated to high temporal and spatial resolution organ-specific imaging is an active research area, motivated by the need for cost reduction, improved lesion detectability and quantification in specific clinical scenarios, as well as by ongoing hardware and software innovations. PURPOSE: This study investigates and compares various image reconstruction strategies for a dual-panel prostate-dedicated PET scanner (ProVision), which features four-layered dual-readout time-of-flight depth-of-interaction detectors and a 22-position acquisition protocol to improve angular coverage. MATERIALS AND
methodsA list-mode MLEM algorithm with multi-ray modeling was developed and optimized using a scaled NEMA image quality phantom to determine optimal number of rays and iterations. These parameters were then used to reconstruct data from both simulation and experimental acquisitions, including an anthropomorphic pelvis phantom, named Adam-PETer. Four reconstruction approaches were evaluated: classical MLEM; MLEM with embedded shift-variant point spread function (PSF) modeling; a hybrid list-mode reconstruction; and a Swin-UNETR-based deep learning model applied as a post-reconstruction enhancement to MLEM images. Performance was assessed using contrast recovery coefficient (CRC), contrast-to-noise ratio (CNR), and contrast-to-noise consistency (CNC), on both a scaled NEMA phantom and an experimental anthropomorphic phantom.
resultsIn the scaled NEMA phantom simulation, the Swin-based method yielded the highest CNR and CNC, especially for the smallest spheres, thereby outperforming both standard MLEM and the hybrid algorithm. In the Adam-PETer experimental prostate phantom, the CNR was 10.43 for MLEM, 14.48 for Hybrid, and 13.85 for Swin for the larger lesion (10 mm). The CNR values were 2.28, 3.03, and 4.35, respectively, for the smaller lesion (8 mm). CNC values also varied across methods, with Swin achieving the best result for the smaller lesion. These findings indicate that model-based and learned methods offer complementary strengths depending on lesion contrast and size.
conclusionThe PET scanner-adapted reconstruction combined with deep learning refinement improves image quality in dedicated, limited-angle PET systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.