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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 1: AAAI-21 Technical Tracks 1

Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

February 1, 2023

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Authors

Chun-Mei Feng

Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology (Shenzhen), China


Zhanyuan Yang

School of Automation Engineering, University of Electronic Science and Technology of China, China


Geng Chen

Inception Institute of Artificial Intelligence, Abu Dhabi, UAE


Yong Xu

Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology (Shenzhen), China Peng Cheng Laboratory, Shenzhen, China


Ling Shao

Inception Institute of Artificial Intelligence, Abu Dhabi, UAE


DOI:

10.1609/aaai.v35i1.16084


Abstract:

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging has always been the subject of research. In this paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable of learning multi-scale spatial-frequency features from both real and imaginary components, for fast parallel MR image reconstruction. By reformulating the complex operations using octave convolutions, our model shows a strong ability to capture richer representations of MR images, while at the same time greatly reducing the spatial redundancy. More specifically, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), which are then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides two appealing benefits: (i) it encourages interactions between real and imaginary components at various spatial frequencies to achieve richer representational capacity, and (ii) it enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction. Extensive experiments are conducted on an {in vivo} knee dataset under different undersampling patterns and acceleration factors. The experimental results demonstrate the superiority of our model in accelerated parallel MR image reconstruction. Our code is available at: github.com/chunmeifeng/Dual-OctConv.

Topics: AAAI

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HOW TO CITE:

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction Proceedings of the AAAI Conference on Artificial Intelligence (2021) 116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction AAAI 2021, 116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao (2021). Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction. Proceedings of the AAAI Conference on Artificial Intelligence, 116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao. Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao. 2021. Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction. "Proceedings of the AAAI Conference on Artificial Intelligence". 116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao. (2021) "Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction", Proceedings of the AAAI Conference on Artificial Intelligence, p.116-124

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao, "Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction", AAAI, p.116-124, 2021.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao. "Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao. "Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 116-124.

Chun-Mei Feng||Zhanyuan Yang||Geng Chen||Yong Xu||Ling Shao. Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction. AAAI[Internet]. 2021[cited 2023]; 116-124.


ISSN: 2374-3468


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