ArticleEuropean journal of nuclear medicine and molecular imaging2024
Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.
Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Estimation and correction of respiratory motion in pediatric renal SPECT through a data-driven center-of-mass method.Medical physics · 2026Article
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- Precision Cardio-Oncology and Nuclear Imaging: Current Applications, Molecular Innovations, and Future Trajectories.Cancers · 2026Review
- Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.NPJ digital medicine · 2026Article
- Impact of Deep-Learning-Based Respiratory Motion Correction on [Biomedicines · 2026Article
- Clinical validation of a unified data-driven respiratory motion correction technique inEJNMMI physics · 2026Article
- Enhanced attenuation correction in FDG-PET/CT with respiratory mismatch correction.EJNMMI reports · 2025Article
- Motion study on upright patient positioning in walk-through PET: evaluation in a clinical setting.EJNMMI physics · 2025Article
- Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF).EJNMMI research · 2025Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- Quantitative analysis of patient motion in walk-through PET scanner and standard axial field of view pet scanner using infrared-based tracking.EJNMMI physics · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeRespiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment.
methodsIn a retrospective study, 737 patients underwent [
resultsuRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUV
conclusionA data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.
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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 Socratic graph.