ArticleEuropean journal of nuclear medicine and molecular imaging2026
Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.
Article in European journal of nuclear medicine and molecular imaging, 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.
- PSMA PET/CT improves precision diagnosis, treatment, and clinical decision-making in prostate cancer.American journal of translational research · 2026Review
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
purposeEfforts to reduce the radiation burden of PET/CT have driven the increasing development of AI-based CT-less PET imaging techniques. However, comprehensive clinical evaluations of these approaches remain limited. This study aimed to rigorously assess whether deep learning (DL)-based PET reconstruction can eliminate the need for CT-derived attenuation and scatter correction while maintaining image quality sufficient for reliable clinical diagnosis.
methodsIn this dual-center retrospective analysis, raw PET/CT data from 359 patients were evaluated across 4 scanners and 4 tracers. Each dataset underwent four reconstruction approaches: (1) CT-based attenuation and scatter correction (CT-ASC, reference standard); (2) conventional 2D-DL; (3) conventional 3D-DL; and (4) our novel Decomposition-based DL algorithm. Diagnostic quality of reconstructed images was systematically assessed via visual scoring (5-point Likert scale), diagnostic accuracy (lesion-based false-positive/negative rates), and semi-quantitative metrics (SUVmax consistency).
resultsVisual analysis demonstrated the superior performance of Decomposition-based DL compared to conventional 2D-DL and 3D-DL (p < 0.001 for all comparisons). Furthermore, the proposed method exhibited the lowest false-negative and false-positive rates (0.56% false positives with SIEMENS Vision 600; zero rates in other cases). Semi-quantitative analysis showed that although Decomposition-based DL did not consistently yield the lowest mean absolute percentage error values compared to controls, it maintained strong agreement with CT-ASC in most cases.
conclusionThis dual-center study demonstrates that decomposition-based DL CT-free PET imaging outperforms conventional DL methods, achieving diagnostic accuracy comparable to CT-based attenuation correction in most cases. This clinical evaluation provides valuable insights to guide further methodological development and support clinical translation of CT-free PET imaging.
Indexed as
Identifiers
41091188What 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.