Prospective Validation of Vesical Imaging-Reporting and Data System (VI-RADS) Using a Next-Generation Magnetic Resonance Imaging Scanner: Is Denoising Deep Learning Reconstruction Useful?

Satoru Taguchi,Mitsuhiro Tambo,Masanaka Watanabe,Haruhiko Machida,Toshiya Kariyasu,Keita Fukushima,Yuta Shimizu,Takatsugu Okegawa,Kenichi Yokoyama,Hiroshi Fukuhara

Prospective Validation of Vesical Imaging-Reporting and Data System (VI-RADS) Using a Next-Generation Magnetic Resonance Imaging Scanner: Is Denoising Deep Learning Reconstruction Useful?

2020

Satoru Taguchi
Mitsuhiro Tambo
Masanaka Watanabe
Haruhiko Machida
Toshiya Kariyasu
Keita Fukushima
Yuta Shimizu
Takatsugu Okegawa
Kenichi Yokoyama
Hiroshi Fukuhara

Purpose:The Vesical Imaging Reporting and Data System (VI-RADS) was launched in 2018 to standardize reporting of magnetic resonance imaging (MRI) for bladder cancer (BC). This study aimed to prospe...

Keywords:

Scanner
Artificial intelligence
Nuclear medicine
Noise reduction
Bladder cancer
Deep learning
Magnetic resonance imaging
Medicine

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