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Abstract #4306

Multi-Adaptive Convolutional Neural Network Reconstruction (MA-CNNR) for Parallel Imaging at 1.5T Brain Images

Yukio Kaneko1, Atsuro Suzuki1, Tomoki Amemiya1, Chizue Ishihara1, Yoshitaka Bito2, and Toru Shirai1
1Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan, 2Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan


Recently, deep learning techniques for high-speed or high-quality imaging in MRI have been reported. However, deep learning techniques for the inhomogeneous spatial distribution of noise caused by parallel imaging have not been fully established. In this study, “Multi-Adaptive Convolutional Neural Network Reconstruction (MA-CNNR)” has been investigated. A noisy image was segmented into four regions by g-factor map, and different optimized CNNs were selected for each region. A denoised image was generated by combining the four denoised regions. The denoising effect was evaluated for 1.5T brain images, and it was confirmed that MA-CNNR can reduce the inhomogeneous noise in parallel imaging.

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