ArticleMedical physics2018
Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging.
Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 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
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Accuracy and Longitudinal Consistency of PET/MR Attenuation Correction in Amyloid PET Imaging amid Software and Hardware Upgrades.AJNR. American journal of neuroradiology · 2025Article
- Deep-learning-based Attenuation Correction forMolecular imaging and radionuclide therapy · 2024Article
- Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.EJNMMI physics · 2024Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- On the effect of training database size for MR-based synthetic CT generation in the head.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB).Magnetic resonance in medicine · 2022Article
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- Clinical application of deep learning-based synthetic CT from real MRI to improve dose planning accuracy in Gamma Knife radiosurgery: a proof of concept study.Biomedical engineering letters · 2022Article
- Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.Medical image analysis · 2022Article
- MRI classification using semantic random forest with auto-context model.Quantitative imaging in medicine and surgery · 2021Article
- Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.PET clinics · 2021Review
- Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.Magnetic resonance in medicine · 2021Article
- Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021Review
- Characterization of hardware-related spatial distortions for IR-PETRA pulse sequence using a brain specific phantom.Magma (New York, N.Y.) · 2021Article
- MR-based Attenuation Correction for Brain PET Using 3D Cycle-Consistent Adversarial Network.IEEE transactions on radiation and plasma medical sciences · 2021Article
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Attenuation correction for human PET/MRI studies.Physics in medicine and biology · 2020Article
- Deep-JASC: joint attenuation and scatter correction in whole-bodyEuropean journal of nuclear medicine and molecular imaging · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeIn this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition.
methodsMR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 s). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on eight human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image.
resultDice coefficients for air (within the head), soft tissue, and bone labels were 0.76 ± 0.03, 0.96 ± 0.006, and 0.88 ± 0.01. In PET quantitation, the proposed MRAC method produced relative PET errors less than 1% within most brain regions.
conclusionThe proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantitation with accurate and rapid pseudo CT generation.
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Registered trials
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.