Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography-Reference-Cited by-同舟云学术

Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography

Published:2022-03-01 Issue:2 Volume:4 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Hahn Lewis D.¹^ORCID,Hall Kent¹,Alebdi Thamer¹,Kligerman Seth J.¹^ORCID,Hsiao Albert¹^ORCID

Affiliation:

1. From the Department of Radiology, University of California San Diego School of Medicine, 9300 Campus Point Dr, MC 0841, La Jolla, CA 92037-0841 (L.D.H., T.A., S.J.K., A.H.); and Naval Hospital Camp Pendleton, Oceanside, Calif (K.H.).

Funder

UCSD Health Sciences Research

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.210162

Reference13 articles.

1. How I Do It: CT Pulmonary Angiography

2. Effect of Patient Weight and Scanning Duration on Contrast Enhancement during Pulmonary Multidetector CT Angiography

3. The Indeterminate CT Pulmonary Angiogram: Imaging Characteristics and Patient Clinical Outcome

4. Segmentation and quantification of pulmonary artery for noninvasive CT assessment of sickle cell secondary pulmonary hypertension

5. Automatic segmentation and analysis of the main pulmonary artery on standard post-contrast CT studies using iterative erosion and dilation

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