ArticlePhysics in medicine and biology2020
Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.NPJ digital medicine · 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
- Total-body [European journal of nuclear medicine and molecular imaging · 2026Article
- Attenuation correction of cardiacEJNMMI physics · 2026Article
- Generative restoration for cardiac PET attenuation correction: a two-stage 3D DDIM framework optimizing fidelity and clinical controllability.EJNMMI physics · 2026Article
- Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR.American journal of nuclear medicine and molecular imaging · 2026Article
- An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction.IEEE transactions on radiation and plasma medical sciences · 2025Article
- 2.5D Multi-View Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-Count PET Reconstruction With CT-Less Attenuation Correction.IEEE transactions on medical imaging · 2025Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.Radiological physics and technology · 2025Article
- Optimizing Attenuation Correction inDiagnostics (Basel, Switzerland) · 2025Article
- POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.IEEE transactions on medical imaging · 2025Article
- Targeted radioligand therapy: physics and biology, internal dosimetry and other practical aspects duringTheranostics · 2025Review
- CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data.Diagnostics (Basel, Switzerland) · 2024Article
- Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging.Physics in medicine and biology · 2024Article
- Deep-learning-based Attenuation Correction forMolecular imaging and radionuclide therapy · 2024Article
- Learning CT-free attenuation-corrected total-body PET images through deep learning.European radiology · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.Oncotarget · 2024Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensities to PET µ-map values (e.g. modality artifacts, implanted devices, or contrast agents). This work presents a deep learning-based attenuation correction (DL-AC) method to generate attenuation corrected PET (AC PET) from non-attenuation corrected PET (NAC PET) images for whole-body PET imaging, without the use of structural information. 3D patch-based cycle-consistent generative adversarial networks (CycleGAN) is introduced to include NAC-PET-to-AC-PET mapping and inverse mapping from AC PET to NAC PET, which constrains NAC-PET-to-AC-PET mapping to be closer to one-to-one mapping. Since NAC PET images share similar anatomical structures to the AC PET image but lack contrast information, residual blocks, which aim to learn the differences between NAC PET and AC PET, are used to construct generators of CycleGAN. After training, patches from NAC PET images were fed into NAC-PET-to-AC-PET mapping to generate DL-AC PET patches. DL-AC PET image was then reconstructed through patch fusion. We conducted a retrospective study on 55 datasets of whole-body PET/CT scans to evaluate the proposed method. In comparing DL-AC PET with original AC PET, average mean error (ME) and normalized mean square error (NMSE) of the whole-body were 0.62% ± 1.26% and 0.72% ± 0.34%. The average intensity changes measured on sequential PET images with AC and DL-AC on both normal tissues and lesions differ less than 3%. There was no significant difference of the intensity changes between AC and DL-AC PET, which demonstrate DL-AC PET images generated by the proposed DL-AC method can reach a same level to that of original AC PET images. The method demonstrates excellent quantification accuracy and reliability and is applicable to PET data collected on a single PET scanner or hybrid platform with computed tomography (PET/CT) or magnetic resonance imaging (PET/MRI).
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