Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI.

ABSTRACT:

Background

Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor-intensive lung segmentation.

Purpose

To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI.

Study type

Retrospective study aimed to evaluate a technical development.

Population

Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis.

Field strength/sequence

1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence.

Assessment

Two 3D ra

SUBMITTER: Zha W 

PROVIDER: S-EPMC7039686 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature