ArticleEJNMMI physics2018
A deep learning approach for
Article in EJNMMI physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it, 131 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
- Realization of long axial field-of-view PET technology in the clinic and research environment.The British journal of radiology · 2026Review
- Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026Review
- Article
- Evaluation of regularization factor and attenuation correction method of Q.Clear reconstruction for brain PET/MR in epileptic patients.EJNMMI physics · 2026Article
- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 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
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- 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
- An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction.IEEE transactions on radiation and plasma medical sciences · 2025Article
- A retrospective comparison of [Physical and engineering sciences in medicine · 2025Article
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- 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
- AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging.Polish journal of radiology · 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
- ImprovingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI physics · 2024Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTo develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously valued pseudo-computed tomography (CT) images from uncorrected
resultsdeepAC produced pseudo-CTs with Dice coefficients of 0.80 ± 0.02 for air, 0.94 ± 0.01 for soft tissue, and 0.75 ± 0.03 for bone and MAE of 111 ± 16 HU relative to the PET/CT dataset. deepAC provides quantitatively accurate
conclusionsWe have developed an automated approach (deepAC) that allows generation of a continuously valued pseudo-CT from a single
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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.