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Ito T, Maeno T, Tsuchikame H, Shishido M, Nishi K, Kojima S, Hayashi T, Suzuki K. Adapting a low-count acquisition of the bone scintigraphy using deep denoising super-resolution convolutional neural network. Phys Med 2022; 100:18-25. [PMID: 35716484 DOI: 10.1016/j.ejmp.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/21/2022] [Accepted: 06/11/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE Deep-layer learning processing may improve contrast imaging with greater precision in low-count acquisition. However, no data on noise reduction using super-resolution processing for deep-layer learning have been reported in nuclear medicine imaging. OBJECTIVES This study was designed to evaluate the adaptability of deep denoising super-resolution convolutional neural networks (DDSRCNN) in nuclear medicine by comparing them with denoising convolutional natural networks (DnCNN), Gaussian processing, and nonlinear diffusion (NLD) processing. METHODS In this study, 156 patients were included. Data were collected using a matrix size of 256 × 256 with a pixel size of 2.46 mm at 0.898 folds, 15% energy window at the center of the photopeak energy (140 keV), and total count of 1000 kilocounts (kct). Following the training and validation of two learning models, we created 100 images for each 20-test datum. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between each image and the reference image were calculated. RESULTS DDSRCNN showed the highest PSNR values for all total counts. Regarding SSIM, DDSRCNN had significantly higher values than the original and Gaussian. In DnCNN, false accumulation was observed as the total counts increased. Regarding PSNR and SSIM transition, the model using 100-500-kct training data was significantly higher than that using 100-kct training data. CONCLUSIONS Edge-preserving noise reduction processing was possible, and adaptability to low-count acquisition was demonstrated using DDSRCNN. Using training data with different noise levels, DDSRCNN could learn the noise components with high accuracy and contrast improvement.
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Affiliation(s)
- Toshimune Ito
- Department of Radiological, Technology, Faculty of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8605, Japan.
| | - Takafumi Maeno
- Department of Radiology, Saiseikai Yokohamashi Tobu Hospital, 3-6-1 Shimosueyoshi, Tsurumi-ku, Yokohama, Kanagawa 230-0012, Japan.
| | - Hirotatsu Tsuchikame
- Department of Radiology, Saiseikai Yokohamashi Tobu Hospital, 3-6-1 Shimosueyoshi, Tsurumi-ku, Yokohama, Kanagawa 230-0012, Japan.
| | - Masaaki Shishido
- Department of Radiology, Saiseikai Yokohamashi Tobu Hospital, 3-6-1 Shimosueyoshi, Tsurumi-ku, Yokohama, Kanagawa 230-0012, Japan.
| | - Kana Nishi
- Department of Radiology, Saiseikai Yokohamashi Tobu Hospital, 3-6-1 Shimosueyoshi, Tsurumi-ku, Yokohama, Kanagawa 230-0012, Japan
| | - Shinya Kojima
- Department of Radiological, Technology, Faculty of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8605, Japan.
| | - Tatsuya Hayashi
- Department of Radiological, Technology, Faculty of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8605, Japan.
| | - Kentaro Suzuki
- Department of Radiological Technology, Toranomon Hospital, 2-2-2 Tranomon, Minato-ku, Tokyo 105-8470, Japan; Department of Radiation Oncology, Graduated School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, Japan.
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