ArticleNature communications2022
Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 54 citations in OpenAlex.
- Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026Review
- Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning.NMR in biomedicine · 2026Article
- Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.NPJ digital medicine · 2026Article
- Article
- Evaluation of regularization factor and attenuation correction method of Q.Clear reconstruction for brain PET/MR in epileptic patients.EJNMMI physics · 2026Article
- Persistent lung and vascular inflammation in mild to moderate COVID-19 survivors detected by ¹⁸F-FDG PET/CT: A quantitative imaging study with implications for cardiovascular risk.European journal of nuclear medicine and molecular imaging · 2026Article
- Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.European journal of nuclear medicine and molecular imaging · 2026Article
- PET scout: A valuable technology in pediatricEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- Human subject study of a transmission-aided attenuation correction for precision PET neuroimaging.EJNMMI physics · 2025Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- Total-body positron emission tomography imaging to accelerate radiotracer discovery pipelines.Pharmacological reviews · 2025Review
- Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.European journal of nuclear medicine and molecular imaging · 2025Article
- Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.European journal of nuclear medicine and molecular imaging · 2025Article
- Evaluation of deep learning-based scatter correction on a long-axial field-of-view PET scanner.European journal of nuclear medicine and molecular imaging · 2025Article
- Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.European journal of nuclear medicine and molecular imaging · 2025Article
- Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.European journal of nuclear medicine and molecular imaging · 2025Article
- Sedation-free pediatric [European journal of nuclear medicine and molecular imaging · 2024Article
- Learning CT-free attenuation-corrected total-body PET images through deep learning.European radiology · 2024Article
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI physics · 2024Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation.
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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.