Tong Zheng1, Hirohisa Oda1, Yuichiro Hayashi1, Takayasu Moriya1, Shota Nakamura2, Masaki Mori3, Hirotsugu Takabatake4, Hiroshi Natori5, Masahiro Oda1,6, Kensaku Mori1,7,8. 1. Nagoya University, Graduate School of Informatics, Furo-cho, Chikusa-ku, Nagoya, Japan. 2. Nagoya University, Graduate School of Medicine, Nagoya, Japan. 3. Sapporo-Kosei General Hospital, Sapporo, Japan. 4. Sapporo Minami-Sanjo Hospital, Sapporo, Japan. 5. Keiwakai Nishioka Hospital, Sapporo, Japan. 6. Nagoya University, Information Strategy Office, Information and Communications, Nagoya, Japan. 7. Nagoya University, Information Technology Center, Nagoya, Japan. 8. National Institute of Informatics, Research Center of Medical BigData, Tokyo, Japan.
Abstract
Purpose: We propose a super-resolution (SR) method, named SR-CycleGAN, for SR of clinical computed tomography (CT) images to the micro-focus x-ray CT CT ( μ CT ) level. Due to the resolution limitations of clinical CT (about 500 × 500 × 500 μ m 3 / voxel ), it is challenging to obtain enough pathological information. On the other hand, μ CT scanning allows the imaging of lung specimens with significantly higher resolution (about 50 × 50 × 50 μ m 3 / voxel or higher), which allows us to obtain and analyze detailed anatomical information. As a way to obtain detailed information such as cancer invasion and bronchioles from preoperative clinical CT images of lung cancer patients, the SR of clinical CT images to the μ CT level is desired. Approach: Typical SR methods require aligned pairs of low-resolution (LR) and high-resolution images for training, but it is infeasible to obtain precisely aligned paired clinical CT and μ CT images. To solve this problem, we propose an unpaired SR approach that can perform SR on clinical CT to the μ CT level. We modify a conventional image-to-image translation network named CycleGAN to an inter-modality translation network named SR-CycleGAN. The modifications consist of three parts: (1) an innovative loss function named multi-modality super-resolution loss, (2) optimized SR network structures for enlarging the input LR image to 2 k -times by width and height to obtain the SR output, and (3) sub-pixel shuffling layers for reducing computing time. Results: Experimental results demonstrated that our method successfully performed SR of lung clinical CT images. SSIM and PSNR scores of our method were 0.54 and 17.71, higher than the conventional CycleGAN's scores of 0.05 and 13.64, respectively. Conclusions: The proposed SR-CycleGAN is usable for the SR of a lung clinical CT into μ CT scale, while conventional CycleGAN output images with low qualitative and quantitative values. More lung micro-anatomy information could be observed to aid diagnosis, such as the shape of bronchioles walls.
Purpose: We propose a super-resolution (SR) method, named SR-CycleGAN, for SR of clinical computed tomography (CT) images to the micro-focus x-ray CT CT ( μ CT ) level. Due to the resolution limitations of clinical CT (about 500 × 500 × 500 μ m 3 / voxel ), it is challenging to obtain enough pathological information. On the other hand, μ CT scanning allows the imaging of lung specimens with significantly higher resolution (about 50 × 50 × 50 μ m 3 / voxel or higher), which allows us to obtain and analyze detailed anatomical information. As a way to obtain detailed information such as cancer invasion and bronchioles from preoperative clinical CT images of lung cancer patients, the SR of clinical CT images to the μ CT level is desired. Approach: Typical SR methods require aligned pairs of low-resolution (LR) and high-resolution images for training, but it is infeasible to obtain precisely aligned paired clinical CT and μ CT images. To solve this problem, we propose an unpaired SR approach that can perform SR on clinical CT to the μ CT level. We modify a conventional image-to-image translation network named CycleGAN to an inter-modality translation network named SR-CycleGAN. The modifications consist of three parts: (1) an innovative loss function named multi-modality super-resolution loss, (2) optimized SR network structures for enlarging the input LR image to 2 k -times by width and height to obtain the SR output, and (3) sub-pixel shuffling layers for reducing computing time. Results: Experimental results demonstrated that our method successfully performed SR of lung clinical CT images. SSIM and PSNR scores of our method were 0.54 and 17.71, higher than the conventional CycleGAN's scores of 0.05 and 13.64, respectively. Conclusions: The proposed SR-CycleGAN is usable for the SR of a lung clinical CT into μ CT scale, while conventional CycleGAN output images with low qualitative and quantitative values. More lung micro-anatomy information could be observed to aid diagnosis, such as the shape of bronchioles walls.
Authors: Chenyu You; Wenxiang Cong; Michael W Vannier; Punam K Saha; Eric A Hoffman; Ge Wang; Guang Li; Yi Zhang; Xiaoliu Zhang; Hongming Shan; Mengzhou Li; Shenghong Ju; Zhen Zhao; Zhuiyang Zhang Journal: IEEE Trans Med Imaging Date: 2019-06-14 Impact factor: 10.048
Authors: Indranil Guha; Syed Ahmed Nadeem; Chenyu You; Xiaoliu Zhang; Steven M Levy; Ge Wang; James C Torner; Punam K Saha Journal: Proc SPIE Int Soc Opt Eng Date: 2020-02-28