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

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 significa...

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Detalles Bibliográficos
Autores principales: Sagawa, Hajime, Fushimi, Yasutaka, Nakajima, Satoshi, Fujimoto, Koji, Miyake, Kanae Kawai, Numamoto, Hitomi, Koizumi, Koji, Nambu, Masahito, Kataoka, Hiroharu, Nakamoto, Yuji, Saga, Tsuneo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japanese Society for Magnetic Resonance in Medicine 2020
Materias:
Technical Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922344/
https://www.ncbi.nlm.nih.gov/pubmed/32963184
http://dx.doi.org/10.2463/mrms.tn.2020-0061
Descripción
Sumario: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.

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