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Abstract #3548

Evaluation of noise reduction performance using deep learning reconstruction: A phantom study

Hitoshi Kubo1, Yuya Abe2, Tomoya Yokokawa2, Seira Yokoyama2, and Koji Hoshi2
1Fukushima Medical University, Fukushima, Japan, 2Hoshi General Hospital, Koriyama, Japan

We aim to assess fundamental noise reduction performance using deep learning reconstruction with a phantom at a 1.5 T MR scanner. In this study, the relationship among parameters for noise reduction, signal-to-noise ratio, image quality, and spatial resolution of images was evaluated. SNRs were increased higher significantly by DLR in all SNR ranges. Increasing ratio of SNR was varied by means of parameter settings. Combination of the DLR parameters affected varies to SNR, SSIM, and spatial resolution of the images. We should exercise caution to select DLR parameters when this technique applies to clinical images.

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