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147 results on '"Nakamoto, Takahiro"'

6. 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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12. 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

14. Effect of Sacubitril/Valsartan on Patients Having Heart Failure With Preserved Left Ventricular Ejection Fraction Undergoing Hemodialysis: A Long-term Observational Study

Author

MIMA, AKIRA, primary, GOTODA, HIDEMASA, additional, LEE, SHINJI, additional, LEE, RINA, additional, MURAKAMI, AMI, additional, AKAI, RYOSUKE, additional, KIDOOKA, SAYUMI, additional, MATSUMOTO, KEISHI, additional, SAITO, YUTA, additional, HISHIDA, SHINJI, additional, NAKAMOTO, TAKAHIRO, additional, KIDO, SUGURU, additional, and HAMADA, TAKAYOSHI, additional

Published
2024
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16. ASO Visual Abstract: Development and Validation of a Preoperative Nomogram for Endoscopic Management Decision Making in Upper Urinary Tract Urothelial Carcinoma

Author

Nakamoto, Takahiro, primary, Yoshida, Takashi, additional, Katayama, Satoshi, additional, Ohe, Chisato, additional, Kawaura, Takayuki, additional, Horii, Satoshi, additional, Ikeda, Junichi, additional, Kono, Yumiko, additional, Murota, Takashi, additional, Kitawaki, Tomoki, additional, Araki, Motoo, additional, and Kinoshita, Hidefumi, additional

Published
2023
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19. Correction: Development and Validation of a Preoperative Nomogram for Endoscopic Management Decision Making in Upper Urinary Tract Urothelial Carcinoma

Author

Nakamoto, Takahiro, primary, Yoshida, Takashi, additional, Katayama, Satoshi, additional, Ohe, Chisato, additional, Kawaura, Takayuki, additional, Horii, Satoshi, additional, Ikeda, Junichi, additional, Kono, Yumiko, additional, Murota, Takashi, additional, Kitawaki, Tomoki, additional, Araki, Motoo, additional, and Kinoshita, Hidefumi, additional

Published
2023
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29. Learning from the past and present to change the future: Endoscopic management of upper urinary tract urothelial carcinoma.

Author

Yoshida, Takashi, Ohe, Chisato, Nakamoto, Takahiro, and Kinoshita, Hidefumi

Subjects
ENDOSCOPIC surgery, URINARY organs, TRANSITIONAL cell carcinoma, OPERATIVE surgery, PATIENT selection
Abstract

Current guidelines recommend endoscopic management (EM) for patients with low‐risk upper urinary tract urothelial carcinoma, as well as those with an imperative indication. However, regardless of the tumor risk, radical nephroureterectomy is still mainly performed worldwide despite the benefits of EM, such as renal function maintenance, no hemodialysis requirement, and treatment cost reduction. This might be explained by the association of EM with a high risk of local recurrence and progression. Furthermore, the need for rigorous patient selection and close surveillance following EM may be relevant. Nevertheless, recent developments in diagnostic modalities, pathological evaluation, surgical devices and techniques, and intracavitary regimens have been reported, which may contribute to improved risk stratification and treatments with superior oncological outcomes. In this review, considering recent advances in endourology and oncology, we propose novel treatment strategies for optimal EM. [ABSTRACT FROM AUTHOR]

Published
2023
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34. Training of deep cross‐modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion

Author

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

Published
2022
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36. Training of deep cross‐modality conversion models with a small data set, 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
PROBABILISTIC generative models, DEEP learning, PHYSICIANS, GENERATIVE adversarial networks, COMPUTED tomography, DIGITAL preservation, HEAD & neck cancer
Abstract

Purpose: 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 data set 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. Methods: The proposed method is based on cycle‐consistency generative adversarial network (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 data sets acquired from patients with head and neck cancer. The size of the data sets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. Results: 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. Conclusions: 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?" [ABSTRACT FROM AUTHOR]

Published
2022
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40. NIMG-11. VISUALIZATION OF JUDGMENT BASIS OF CNN TO GRADING GLIOMA

Author

Takahashi, Satoshi, primary, Tanaka, Shota, additional, Takahashi, Masamichi, additional, Yamazawa, Erika, additional, Hana, Taijun, additional, Kitagawa, Yosuke, additional, Takayanagi, Shunsaku, additional, Takahashi, Wataru, additional, Nakamoto, Takahiro, additional, Haga, Akihirio, additional, Hamamoto, Ryuji, additional, and Saito, Nobuhito, additional

Published
2019
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41. RARE-16. A NOVEL RADIOMICS MODEL DIFFERENTIATING CHORDOMA AND CHONDROSARCOMA

Author

Yamazawa, Erika, primary, Takahashi, Satoshi, additional, Tanaka, Shota, additional, Takahashi, Wataru, additional, Nakamoto, Takahiro, additional, Takayanagi, Shunsaku, additional, Kitagawa, Yosuke, additional, Hana, Taijun, additional, Koike, Tsukasa, additional, Kushihara, Yoshihiro, additional, Shin, Masahiro, additional, and Saito, Nobuhito, additional

Published
2019
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42. Response to Comment: Future goals of upper tract urothelial carcinoma endoscopic management.

Author

Nakamoto, Takahiro and Yoshida, Takashi

Subjects
*ENDOSCOPIC surgery, *TRANSITIONAL cell carcinoma, *OPERATIVE surgery
Abstract

This article is a response to a comment made by Dr. Jue regarding the future goals of upper tract urothelial carcinoma (UTUC) endoscopic management. The authors agree with Dr. Jue's opinion that the high rates of understaging and undergrading are major issues in selecting candidates for endoscopic management. They propose a comprehensive risk stratification system that integrates multiple clinical factors to determine indications for endoscopic management. The authors also discuss a novel en-bloc enucleation technique for low-grade and nonmuscle-invasive UTUC, highlighting potential issues and the need for further exploration of accurate and effective surgical techniques. [Extracted from the article]

Published
2024
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43. NIMG-55. RADIOMICS ANALYSIS FOR DETECTION OF IDH MUTATION OF GLIOMA USING DIFFUSION TENSOR AND KURTOSIS IMAGES

Author

Takahashi, Satoshi, primary, Takahashi, Wataru, additional, Tanaka, Shota, additional, Haga, Akihiro, additional, Nakamoto, Takahiro, additional, Mukasa, Akitake, additional, Takayanagi, Shunsaku, additional, Suzuki, Yuichi, additional, Koike, Tsukasa, additional, Kitagawa, Yosuke, additional, Hana, Taijyun, additional, Nejo, Takahide, additional, Nomura, Masashi, additional, and Saito, Nobuhito, additional

Published
2018
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47. Comparison of Nephroscope-assisted “Pulling Thread” Technique and Conventional Open Placement of Peritoneal Dialysis Catheters in Patients With End-stage Renal Disease

Author

Yoshida, Takashi, primary, Nakamoto, Takahiro, additional, Yoshida, Kenji, additional, Yanishi, Masaaki, additional, Inoue, Takaaki, additional, Murota, Takashi, additional, Kinoshita, Hidefumi, additional, Tsukaguchi, Hiroyasu, additional, and Matsuda, Tadashi, additional

Published
2016
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50. Isothermal capacitance transient spectroscopy of pseudomorphic high-electron-mobility transistors

Author

Nakamoto Takahiro, Tatsuo Ozeki, Yuji Abe, Naohito Yoshida, and Shigemitsu Maruno

Subjects
Materials science, Physics and Astronomy (miscellaneous), business.industry, Transistor, Hardware_PERFORMANCEANDRELIABILITY, Capacitance, Spectral line, Isothermal process, law.invention, Gallium arsenide, chemistry.chemical_compound, chemistry, Hardware_GENERAL, law, Hardware_INTEGRATEDCIRCUITS, Optoelectronics, business, Hardware_LOGICDESIGN, Surface states, Voltage, Leakage (electronics)
Abstract

The surface electronic properties of AlGaAs/InGaAs pseudomorphic high-electron-mobility transistors were investigated by isothermal capacitance transient spectroscopy (ICTS) and gate-leakage current characteristic measurements. Both hole- and electron-like trap spectra were observed by ICTS measurements on gate–source/drain capacitance. We observed enhancement of leakage current and drastic change of static and transient capacitance behavior around a pinch-off voltage. The leakage characteristics and ICTS results were explained in terms of a surface states model.

Published
2003

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