CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels. (November 2020)

• Record Type: Journal Article
• Authors: Eun, Da-in; Woo, Ilsang; Park, Beomhee; Kim, Namkug; Lee A, Sang Min; Seo, Joon Beom
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Convolutional neural net -- Computed tomography -- Kernel conversion -- Squeeze-and-excitation -- Super resolution; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105615
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: CT kernel conversions using convolutional neural network for super-resolution. Progressive learning scheme that use mutual relations between different CT kernel images. The proposed method can convert one CT kernel images to several CT images with different kernels. Abstract: Purpose: Computed tomography (CT) volume sets reconstructed with different kernels are helping to increase diagnostic accuracy. However, several CT volumes reconstructed with different kernels are difficult to sustain, due to limited storage and maintenance issues. A CT kernel conversion method is proposed using convolutional neural networks (CNN). Methods: A total of 3289 CT images from ten patients (five men and five women; mean age, 63.0 ± 8.6 years) were obtained in May 2016 (Somatom Sensation 16, Siemens Medical Systems, Forchheim, Germany). These CT images were reconstructed with various kernels, including B10f (very smooth), B30f (medium smooth), B50f (medium sharp), and B70f (very sharp) kernels. Smooth kernel images were converted into sharp kernel images using super-resolution (SR) network with Squeeze-and-Excitation (SE) blocks and auxiliary losses, and vice versa. In this study, the single-conversion model and multi-conversion model were presented. In case of the single-conversion model, for the one corresponding output image (e.g., B10f to B70), SE-Residual blocks were stacked. For the multi-conversion model, to convert an image into several output images (e.g., B10f to B30f, B50f, and B70f, and vice versa), progressive learning (PL) was employed by calculating auxiliary losses in every four SE-Residual blocks. Through auxiliary losses, the model could learn mutual relationships between different kernel types. The conversion quality was evaluated by the root-mean-square-error (RMSE), structural similarity (SSIM) index and mutual information (MI) between original and converted images. Results: The RMSE (SSIM index, MI) of the multi-conversion model was 4.541 ± 0.688 (0.998 ± 0.001, 2.587 ± 0.137), 27.555 ± 5.876 (0.944 ± 0.021, 1.735 ± 0.137), 72.327 ± 17.387 (0.815 ± 0.053, 1.176 ± 0.096), 8.748 ± 1.798 (0.996 ± 0.002, 2.464 ± 0.121), 9.470 ± 1.772 (0.994 ± 0.003, 2.336 ± 0.133), and 9.184 ± 1.605 (0.994 ± 0.002, 2.342 ± 0.138) in conversion between B10f–B30f, B10f–B50f, B10f–B70f, B70f–B50f, B70f–B30f, and B70f–B10f, respectively, which showed significantly better image quality than the conventional model. Conclusions: We proposed deep learning-based CT kernel conversion using SR network. By introducing simplified SE blocks and PL, the model performance was significantly improved.