Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults-Reference-Cited by-同舟云学术

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Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults

Published:2021-09 Issue:3 Volume:300 Page:539-548
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Zucker Evan J.¹^ORCID,Sandino Christopher M.¹^ORCID,Kino Aya¹,Lai Peng¹^ORCID,Vasanawala Shreyas S.¹^ORCID

Affiliation:

1. From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.).

Funder

National Institutes of Health

NSF Graduate Research Fellowship

GE Healthcare

Google Cloud

Tashia and John Morgridge Faculty Scholar Fund

Stanford Maternal & Child Health Research Institute

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.2021202624

Reference39 articles.

1. Comparison of two single-breath-held 3-D acquisitions with multi-breath-held 2-D cine steady-state free precession MRI acquisition in children with single ventricles

2. Assessment of cardiac volumes using an isotropic whole-heart dual cardiac phase sequence in pediatric patients

3. Left ventricular function assessment using a fast 3D gradient echo pulse sequence: comparison to standard multi-breath hold 2D steady state free precession imaging and accounting for papillary muscles and trabeculations

4. Clinical validation of free breathing respiratory triggered retrospectively cardiac gated cine balanced steady-state free precession cardiovascular magnetic resonance in sedated children

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