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Abstract #1968

Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation

Tianming Du1, Yuemeng Li1, Honggang Zhang2, Stephen Pickup1, Rong Zhou1, Hee Kwon Song1, and Yong Fan1
1Radiology, University of Pennsylvania, Philadelphia, PA, United States, 2Beijing University of Posts and Telecommunications, Beijing, China

Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data. However, existing deep learning-based image reconstruction methods typically apply weight-sharing convolutional neural networks to k-space data without taking into consideration the k-space data’s spatial frequency properties, leading to ineffective learning of the image reconstruction models. To improve image reconstruction performance, we develop a residual Encoder-Decoder network architecture with self-attention layers to adaptively focus on k-space data at different spatial frequencies and channels for interpolating the undersampled k-space data. Experimental results demonstrate that our method achieves significantly better image reconstruction performance than current state-of-the-art techniques.

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