ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2018
Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification.
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 128 citations in OpenAlex.
- Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.AJNR. American journal of neuroradiology · 2024Pooled it
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- A deep learning model for generating [European journal of nuclear medicine and molecular imaging · 2024Article
- ImprovingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.Nuclear medicine and molecular imaging · 2024Article
- A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods.International journal of molecular sciences · 2024Article
- Construction and validation of a brain magnetic resonance imaging template for normal older Koreans.BMC neurology · 2024Article
- Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.Nuclear medicine and molecular imaging · 2024Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images withBrain sciences · 2024Article
- Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.BMC medical imaging · 2024Article
- Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.Journal of applied clinical medical physics · 2024Article
- Image synthesis of interictal SPECT from MRI and PET using machine learning.Frontiers in neurology · 2024Article
- Generation of ConventionalMedicina (Kaunas, Lithuania) · 2023Article
- Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat).Medical image analysis · 2023Article
- MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization.Nuclear medicine and molecular imaging · 2023Article
- Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease.European physical journal plus · 2023Review
- Shifting machine learning for healthcare from development to deployment and from models to data.Nature biomedical engineering · 2022Review
- Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.European journal of nuclear medicine and molecular imaging · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR.
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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.