ArticleEuropean journal of nuclear medicine and molecular imaging2022
Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 28 citations in OpenAlex.
- Dual-view scout scans with deep learning for ultra-low dose attenuation correction in PET.Medical physics · 2026Article
- Zero-TE MRI-based attenuation correction for bone components on chest [Annals of nuclear medicine · 2026Article
- Exploring extended [European journal of nuclear medicine and molecular imaging · 2026Article
- Article
- Human subject study of a transmission-aided attenuation correction for precision PET neuroimaging.EJNMMI physics · 2025Article
- Long Axial Field-of-view PET: A New Era of Quantitative PET Imaging for Theranostic Applications in Clinic.PET clinics · 2025Review
- Total-body positron emission tomography imaging to accelerate radiotracer discovery pipelines.Pharmacological reviews · 2025Review
- The impact of long axial field of view (LAFOV) PET on oncologic imaging.European journal of radiology · 2025Review
- Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging.European journal of nuclear medicine and molecular imaging · 2024Article
- Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.IEEE transactions on medical imaging · 2024Article
- Advances and challenges in immunoPET methodology.Frontiers in nuclear medicine · 2024Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Long axial field of view (LAFOV) PET-CT: implementation in static and dynamic oncological studies.European journal of nuclear medicine and molecular imaging · 2023Review
- Review
- Long-axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Review
- Combined [68 Ga]Ga-PSMA-11 and low-dose 2-[18F]FDG PET/CT using a long-axial field of view scanner for patients referred for [177Lu]-PSMA-radioligand therapy.European journal of nuclear medicine and molecular imaging · 2023Article
- Contrast enhanced CT on PET/CT imaging in clinical routine: an international survey.Frontiers in medicine · 2023Article
- Extending the clinical capabilities of short- and long-lived positron-emitting radionuclides through high sensitivity PET/CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Review
- Deep learning-based attenuation correction for whole-body PET - a multi-tracer study withEuropean journal of nuclear medicine and molecular imaging · 2022Article
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Authors and funding
11 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAttenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators.
methodsData from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of
resultsNo statistically significant difference was observed in rME values for µ-map
conclusionWe describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners.
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