Article Information
PubMed
Published By
Print ISSN
Online ISSN
History
- Received for publication June 23, 2017
- Accepted for publication October 16, 2017
- Published online May 1, 2018.
Article Versions
- previous version (October 30, 2017 - 04:45).
- You are viewing the most recent version of this article.
Copyright & Usage
© 2018 by the Society of Nuclear Medicine and Molecular Imaging.
Author Information
- Andrew P. Leynes1,2,
- Jaewon Yang1,
- Florian Wiesinger3,
- Sandeep S. Kaushik4,
- Dattesh D. Shanbhag4,
- Youngho Seo1,2,
- Thomas A. Hope1,5 and
- Peder E.Z. Larson1,2
- 1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
- 2UC Berkeley–UCSF Graduate Program in Bioengineering, UC Berkeley, Berkeley, California, and UCSF, San Francisco, California
- 3GE Global Research, Munich, Germany
- 4GE Global Research, Bangalore, India; and
- 5Department of Radiology, San Francisco VA Medical Center, San Francisco, California
- For correspondence or reprints contact: Andrew P. Leynes, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 1700 4th St., Byers Hall, Suite 102, San Francisco, CA 94122. E-mail: andrew.leynes{at}ucsf.edu
Statistics from Altmetric.com
Cited By...
Article usage
Jump to section
Related Articles
Cited By...
- Deep Learning for Synthetic CT from Bone MRI in the Head and Neck
- Enhanced Direct Joint Attenuation and Scatter Correction of Whole-Body PET Images via Context-Aware Deep Networks
- Evaluation of Deep Learning-Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images
- Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study
- Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning
- PET/MRI, Part 2: Technologic Principles
- Machine Learning in Nuclear Medicine: Part 2--Neural Networks and Clinical Aspects
- Summary of the First ISMRM-SNMMI Workshop on PET/MRI: Applications and Limitations
- Intelligent Imaging: Artificial Intelligence Augmented Nuclear Medicine
- Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps
- Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network
- Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning