ArticlePhysics in medicine and biology2019
Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it, 139 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Dual-view scout scans with deep learning for ultra-low dose attenuation correction in PET.Medical physics · 2026Article
- Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.NPJ digital medicine · 2026Article
- Learning CT-to-electron-density mapping for radiotherapy treatment planning using neural networks.Radiation oncology (London, England) · 2026Article
- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 2026Article
- Attenuation correction of cardiacEJNMMI physics · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 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
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- A retrospective comparison of [Physical and engineering sciences in medicine · 2025Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- 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
- A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.Radiological physics and technology · 2025Article
- POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.IEEE transactions on medical imaging · 2025Article
- T1-contrast enhanced MRI generation from multi-parametric MRI for glioma patients with latent tumor conditioning.Medical physics · 2025Article
- Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.Medical physics · 2025Article
- CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data.Diagnostics (Basel, Switzerland) · 2024Article
- Deep-learning-based Attenuation Correction forMolecular imaging and radionuclide therapy · 2024Article
4 more citing papers are in PubMed but not listed here.
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9 authors at 1 institution in 1 country.
Funding
Abstract
Attenuation correction (AC) of PET/MRI faces challenges including inter-scan motion, image artifacts such as truncation and distortion, and erroneous transformation of structural voxel-intensities to PET mu-map values. We propose a deep-learning-based method to derive synthetic CT (sCT) images from non-attenuation corrected PET (NAC PET) images for AC on whole-body PET/MRI imaging. A 3D cycle-consistent generative adversarial networks (CycleGAN) framework was employed to synthesize CT images from NAC PET. The method learns a transformation that minimizes the difference between sCT, generated from NAC PET, and true CT. It also learns an inverse transformation such that cycle NAC PET image generated from the sCT is close to true NAC PET image. A self-attention strategy was also utilized to identify the most informative component and mitigate the disturbance of noise. We conducted a retrospective study on a total of 119 sets of whole-body PET/CT, with 80 sets for training and 39 sets for testing and evaluation. The whole-body sCT images generated with proposed method demonstrate great resemblance to true CT images, and show good contrast on soft tissue, lung and bony tissues. The mean absolute error (MAE) of sCT over true CT is less than 110 HU. Using sCT for whole-body PET AC, the mean error of PET quantification is less than 1% and normalized mean square error (NMSE) is less than 1.4%. Average normalized cross correlation on whole body is close to one, and PSNR is larger than 42 dB. We proposed a deep learning-based approach to generate sCT from whole-body NAC PET for PET AC. sCT generated with proposed method shows great similarity to true CT images both qualitatively and quantitatively, and demonstrates great potential for whole-body PET AC in the absence of structural information.
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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.