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Your search keyword '"Hitomi Numamoto"' showing total 2 results
Start Over Author "Hitomi Numamoto" Topic diffusion tensor imaging
2 results on '"Hitomi Numamoto"'

1. Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics

Author

Koji Fujimoto, Koji Koizumi, Hajime Sagawa, Yasutaka Fushimi, Kanae Miyake, Hiroharu Kataoka, Hitomi Numamoto, Satoshi Nakajima, Yuji Nakamoto, Masahito Nambu, and Tsuneo Saga

Subjects
business.industry, Noise reduction, Deep learning, Reproducibility of Results, Benchmarking, Deep Learning, Diffusion Tensor Imaging, Computer Science::Computer Vision and Pattern Recognition, Fractional anisotropy, Image Processing, Computer-Assisted, Image noise, Anisotropy, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Artificial intelligence, Diffusion (business), business, Algorithm, Reliability (statistics), Diffusion MRI, Volume (compression)
Abstract

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.

Published
2021

2. Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics.

Author

Hajime Sagawa, Yasutaka Fushimi, Satoshi Nakajima, Koji Fujimoto, Kanae Kawai Miyake, Hitomi Numamoto, Koji Koizumi, Masahito Nambu, Hiroharu Kataoka, Yuji Nakamoto, and Tsuneo Saga

Subjects
DEEP learning, NOISE control, DIFFUSION tensor imaging, DIFFUSION magnetic resonance imaging, MAGNETIC anisotropy
Abstract

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5. [ABSTRACT FROM AUTHOR]

Published
2021
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