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3. Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion

Author

Ozaki, Sho, Kaji, Shizuo, Nawa, Kanabu, Imae, Toshikazu, Aoki, Atsushi, Nakamoto, Takahiro, Ohta, Takeshi, Nozawa, Yuki, Yamashita, Hideomi, Haga, Akihiro, and Nakagawa, Keiichi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several datasets acquired from patients with head and neck cancer. The size of the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?", Comment: 3+27 pages, 13 figures, version published in Medical Physics

Published
2021
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7. Fast Statistical Iterative Reconstruction for MVCT in TomoTherapy

Author

Ozaki, Sho, Haga, Akihiro, Chao, Edward, Maurer, Calvin, Nawa, Kanabu, Ohta, Takeshi, Nakamoto, Takahiro, Nozawa, Yuki, Magome, Taiki, Nakano, Masahiro, and Nakagawa, Keiichi

Subjects
Physics - Medical Physics
Abstract

Statistical iterative reconstruction is expected to improve the image quality of megavoltage computed tomography (MVCT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this work is to develop a fast iterative reconstruction algorithm by combining several iterative techniques and by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using our statistical iterative reconstruction. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512$\times$512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. An image from the iterative reconstruction in TomoTherapy can be obtained within few seconds by fine-tuning the parameters. The iterative reconstruction with GPU was fast enough for clinical use, and largely improve the MVCT images., Comment: 11 pages, 4 figures

Published
2019

11. Water Cycles in Forests

Author

Kotani, Ayumi, Ohta, Takeshi, Canadell, Josep G., Series Editor, Díaz, Sandra, Series Editor, Heldmaier, Gerhard, Series Editor, Jackson, Robert B., Series Editor, Levia, Delphis F., Series Editor, Schulze, Ernst-Detlef, Series Editor, Sommer, Ulrich, Series Editor, Wardle, David A., Series Editor, Ohta, Takeshi, editor, Hiyama, Tetsuya, editor, Iijima, Yoshihiro, editor, Kotani, Ayumi, editor, and Maximov, Trofim C., editor

Published
2019
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12. Water and Carbon Dynamics in Eastern Siberia: Introduction

Author

Ohta, Takeshi, Maximov, Trofim C., Fedorov, Alexander N., Desyatkin, Alexey R., Canadell, Josep G., Series Editor, Díaz, Sandra, Series Editor, Heldmaier, Gerhard, Series Editor, Jackson, Robert B., Series Editor, Levia, Delphis F., Series Editor, Schulze, Ernst-Detlef, Series Editor, Sommer, Ulrich, Series Editor, Wardle, David A., Series Editor, Ohta, Takeshi, editor, Hiyama, Tetsuya, editor, Iijima, Yoshihiro, editor, Kotani, Ayumi, editor, and Maximov, Trofim C., editor

Published
2019
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13. Protective Effect of Pemafibrate Treatment against Diabetic Retinopathy in Spontaneously Diabetic Torii Fatty Rats

Author

Tanaka, Yoshiaki, primary, Takagi, Rina, additional, Mitou, Shingen, additional, Shimmura, Machiko, additional, Hasegawa, Tetsuya, additional, Amarume, Jota, additional, Shinohara, Masami, additional, Kageyama, Yasushi, additional, Sasase, Tomohiko, additional, Ohta, Takeshi, additional, Muramatsu, Shin-ichi, additional, Kakehashi, Akihiro, additional, and Kaburaki, Toshikatsu, additional

Published
2024
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15. Relationship between hydrogel spacer distribution and dosimetric parameters in linear‐accelerator‐based stereotactic body radiotherapy for prostate cancer

Author

Ohira, Shingo, primary, Yamashita, Hideomi, additional, Minamitani, Masanari, additional, Sawayanagi, Subaru, additional, Ogita, Mami, additional, Imae, Toshikazu, additional, Katano, Atsuto, additional, Nozawa, Yuki, additional, Ohta, Takeshi, additional, Nawa, Kanabu, additional, Nishio, Teiji, additional, Koizumi, Masahiko, additional, and Nakagawa, Keiichi, additional

Published
2024
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18. Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands

Author

Zhou, Huimin, Shao, Junjiong, Liu, Huiying, Du, Zhenggang, Zhou, Lingyan, Liu, Ruiqiang, Bernhofer, Christian, Grünwald, Thomas, Dušek, Jiří, Montagnani, Leonardo, Tagesson, Torbern, Black, Thomas Andrew, Jassal, Rachhpal, Woodgate, William, Biraud, Sébastien, Varlagin, Andrej, Mammarella, Ivan, Gharun, Mana, Shekhar, Ankit, Buchmann, Nina, Manco, Antonio, Magliulo, Enzo, Billesbach, Dave, Silberstein, Richard P., Ohta, Takeshi, Yu, Guirui, Chen, Zhi, Zhang, Yiping, and Zhou, Xuhui

Published
2021
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21. Water and Carbon Dynamics in Eastern Siberia: Concluding Remarks

Author

Ohta, Takeshi, Hiyama, Tetsuya, Canadell, Josep G., Series Editor, Díaz, Sandra, Series Editor, Heldmaier, Gerhard, Series Editor, Jackson, Robert B., Series Editor, Levia, Delphis F., Series Editor, Schulze, Ernst-Detlef, Series Editor, Sommer, Ulrich, Series Editor, Wardle, David A., Series Editor, Ohta, Takeshi, editor, Hiyama, Tetsuya, editor, Iijima, Yoshihiro, editor, Kotani, Ayumi, editor, and Maximov, Trofim C., editor

Published
2019
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26. Increasing contribution of peatlands to boreal evapotranspiration in a warming climate

Author

Helbig, Manuel, Waddington, James Michael, Alekseychik, Pavel, Amiro, Brian D., Aurela, Mika, Barr, Alan G., Black, T. Andrew, Blanken, Peter D., Carey, Sean K., Chen, Jiquan, Chi, Jinshu, Desai, Ankur R., Dunn, Allison, Euskirchen, Eugenie S., Flanagan, Lawrence B., Forbrich, Inke, Friborg, Thomas, Grelle, Achim, Harder, Silvie, Heliasz, Michal, Humphreys, Elyn R., Ikawa, Hiroki, Isabelle, Pierre-Erik, Iwata, Hiroki, Jassal, Rachhpal, Korkiakoski, Mika, Kurbatova, Juliya, Kutzbach, Lars, Lindroth, Anders, Löfvenius, Mikaell Ottosson, Lohila, Annalea, Mammarella, Ivan, Marsh, Philip, Maximov, Trofim, Melton, Joe R., Moore, Paul A., Nadeau, Daniel F., Nicholls, Erin M., Nilsson, Mats B., Ohta, Takeshi, Peichl, Matthias, Petrone, Richard M., Petrov, Roman, Prokushkin, Anatoly, Quinton, William L., Reed, David E., Roulet, Nigel T., Runkle, Benjamin R. K., Sonnentag, Oliver, Strachan, Ian B., Taillardat, Pierre, Tuittila, Eeva-Stiina, Tuovinen, Juha-Pekka, Turner, Jessica, Ueyama, Masahito, Varlagin, Andrej, Wilmking, Martin, Wofsy, Steven C., and Zyrianov, Vyacheslav

Published
2020
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29. Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome

Author

Yuan, Wenping, Liu, Shuguang, Dong, Wenjie, Liang, Shunlin, Zhao, Shuqing, Chen, Jingming, Xu, Wenfang, Li, Xianglan, Barr, Alan, Andrew Black, T, Yan, Wende, Goulden, Mike L, Kulmala, Liisa, Lindroth, Anders, Margolis, Hank A, Matsuura, Yojiro, Moors, Eddy, van der Molen, Michiel, Ohta, Takeshi, Pilegaard, Kim, Varlagin, Andrej, and Vesala, Timo

Subjects
Biological Sciences, Ecology, Bryophyta, Carbon, Carbon Cycle, Carbon Dioxide, Ecosystem, Models, Biological, Photosynthesis, Plants, Taiga
Abstract

The satellite-derived normalized difference vegetation index (NDVI), which is used for estimating gross primary production (GPP), often includes contributions from both mosses and vascular plants in boreal ecosystems. For the same NDVI, moss can generate only about one-third of the GPP that vascular plants can because of its much lower photosynthetic capacity. Here, based on eddy covariance measurements, we show that the difference in photosynthetic capacity between these two plant functional types has never been explicitly included when estimating regional GPP in the boreal region, resulting in a substantial overestimation. The magnitude of this overestimation could have important implications regarding a change from a current carbon sink to a carbon source in the boreal region. Moss abundance, associated with ecosystem disturbances, needs to be mapped and incorporated into GPP estimates in order to adequately assess the role of the boreal region in the global carbon cycle.

Published
2014

33. Greenhouse Gases and Energy Fluxes at Permafrost Zone

Author

Ueyama, Masahito, primary, Iwata, Hiroki, additional, Kobayashi, Hideki, additional, Euskirchen, Eugénie, additional, Merbold, Lutz, additional, Ohta, Takeshi, additional, Machimura, Takashi, additional, Zona, Donatella, additional, Oechel, Walter C., additional, and Schuur, Edward A. G., additional

Published
2020
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41. Morphological analysis of the retina in salt-loaded KK-Ay mice, obese and type 2 diabetic model

Author

Maekawa, Tatsuya, Sasase, Tomohiko, Shinohara, Masami, and Ohta, Takeshi

Subjects
KK-Ay mice, Diabetes, Retinopathy
Abstract

Retinopathy, one of the microvascular complications in diabetes, can cause blindness. Salt-loading is known to exacerbate microvascular damage and may affect retinopathy. In this study, we investigated the effect of salt loading on early lesions of diabetic retinopathy. Male C57BL/6 and KK-Ay mice were salt-loaded with 1% sodium chloride (NaCl)-containing drinking water for 12 weeks. In addition, to determine the effects of high fat and high sucrose, a high fat/high sucrose diet was also fed to the 1% NaCl-loaded group of mice of both strains. Retinal thickness was measured at an arbitrary location from the optic nerve disc, and thinning of the retina was observed in KK-Ay mice compared to C57BL/6 mice. Salt-loading caused retinal thinning in C57BL/6 mice, but not in KK-Ay mice. In KK-Ay mice, the effect of salt-loading may have been masked by the effects of obesity and diabetic status during this experimental period. There was also a small effect of QF on the retina, suggesting that dietary components other than salt loading may affect retinopathy.

Published
2022

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