Deep convolutional neural network for reduction of contrast-enhanced region on CT images

• Published in: Journal of radiation research Vol. 60; no. 5; pp. 586 - 594
• Main Authors: Sumida, Iori, Magome, Taiki, Kitamori, Hideki, Das, Indra J, Yamaguchi, Hajime, Kizaki, Hisao, Aboshi, Keiko, Yamashita, Kyohei, Yamada, Yuji, Seo, Yuji, Isohashi, Fumiaki, Ogawa, Kazuhiko
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 23.10.2019
• Subjects: Artificial neural networks; CAT scans; Computed tomography; Contrast Media - chemistry; Diagnostic imaging; Dose-Response Relationship, Radiation; Humans; Image acquisition; Medical imaging; Medical imaging equipment; Medical research; Neural networks; Neural Networks, Computer; Pixels; Quantitative analysis; Regular Papers; Technology application; Time Factors; Tomography; Tomography, X-Ray Computed; Japan; deep learning; CT; contrast enhancement; convolution neural network
• DOI: 10.1093/jrr/rrz030
• ISSN: 0449-3060; 1349-9157

Abstract This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The transverse images were divided into 64 × 64 pixels. This resulted in 14 723 patches in total for both non-contrast and contrast-enhanced CT image pairs. The proposed CNN model comprises five two-dimensional (2D) convolution layers with one shortcut path. For comparison, the U-net model, which comprises five 2D convolution layers interleaved with pooling and unpooling layers, was used. Training was performed in 24 patients and, for testing of trained models, another 5 patients were used. For quantitative evaluation, 50 regions of interest (ROIs) were selected on the reference contrast-enhanced image of the test data, and the mean pixel value of the ROIs was calculated. The mean pixel values of the ROIs at the same location on the reference non-contrast image and the predicted non-contrast image were calculated and those values were compared. Regarding the quantitative analysis, the difference in mean pixel value between the reference contrast-enhanced image and the predicted non-contrast image was significant (P < 0.0001) for both models. Significant differences in pixels (P < 0.0001) were found using the U-net model; in contrast, there was no significant difference using the proposed CNN model when comparing the reference non-contrast images and the predicted non-contrast images. Using the proposed CNN model, the contrast-enhanced region was satisfactorily reduced.