Validation of Deep Learning–based Augmentation for Reduced 18F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma-Reference-Cited by-同舟云学术

Validation of Deep Learning–based Augmentation for Reduced 18F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma

Published:2021-11-01 Issue:6 Volume:3 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Theruvath Ashok J.¹^ORCID,Siedek Florian¹^ORCID,Yerneni Ketan¹^ORCID,Muehe Anne M.¹^ORCID,Spunt Sheri L.¹^ORCID,Pribnow Allison¹^ORCID,Moseley Michael¹^ORCID,Lu Ying¹,Zhao Qian¹,Gulaka Praveen¹^ORCID,Chaudhari Akshay¹^ORCID,Daldrup-Link Heike E.¹^ORCID

Affiliation:

1. From the Department of Radiology, Molecular Imaging Program at Stanford (A.J.T., F.S., K.Y., A.M.M., M.M., A.C., H.E.D.L.), Department of Pediatrics, Division of Hematology/Oncology, Lucile Packard Children’s Hospital (S.L.S., A.P., H.E.D.L.), and Department of Biomedical Data Science (Y.L., Q.Z.), Stanford University, 725 Welch Rd, Stanford, CA 94304; and Subtle Medical, Menlo Park, Calif (P.G.).

Funder

Eunice Kennedy Shriver National Institute of Child Health and Human Development

Andrew McDonough B+ Foundation

National Institutes of Health

Publisher

Radiological Society of North America (RSNA)

Subject

Artificial Intelligence,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2021200232

Reference47 articles.

1. Diagnostic value of PET/CT for the staging and restaging of pediatric tumors