Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network

• Type of Material: Article
• Authors: Yuncheng Zhong, University of Texas Southwestern Medical Center; Yevgeniy Vinogradskiy, University of Colorado; Liyuan Chen, University of Texas Southwestern Medical Center; Nick Myziuk, Beaumont Health System; Richard Castillo, Emory University; Edward Castillo, Beaumont Healthy System; Thomas Guerrero, Beaumont Health System; Steve Jiang, University of Texas Southwestern Medical Center; Jing Wang, University of Texas Southwestern Medical Center
• Language: English
• Date: 2019-05-01
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 American Association of Physicists in Medicine.
• Final Published Version (URL): http://dx.doi.org/10.1002/mp.13421
• Title of Journal or Parent Work: Medical Physics
• Volume: 46
• Issue: 5
• Start Page: 2323
• End Page: 2329
• Grant/Funding Information: This work was partially supported by grant NIH R01CA200817 (YV, EC, RC, TG) and NIH R01 EB020366 (YZ, LC, JW).
• Keywords: 4DCT lung ventilation imaging; convolutional neural network; Science & Technology; Impact; Life Sciences & Biomedicine; lung functional imaging; Radiology, Nuclear Medicine & Medical Imaging; Images; Functional lung avoidance; CT
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Oncology; Health Sciences, Radiology

Abstract

Purpose: Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration. Methods: A total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images. Results: The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively. Conclusions: The results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty.