PARCEL: Physics-based unsupervised contrastive representation learning for parallel MR imaging

• URL: http://arxiv.org/abs/2202.01494v1
• Date: Thu, 3 Feb 2022 10:09:19 GMT
• Title: PARCEL: Physics-based unsupervised contrastive representation learning for parallel MR imaging
• Authors: Shanshan Wang, Ruoyou Wu, Cheng Li, Juan Zou, Hairong Zheng
• Abstract summary: This paper proposes a physics based unsupervised contrastive representation learning (PARCEL) method to speed up parallel MR imaging. Specifically, PARCEL has three key ingredients to achieve direct deep learning from the undersampled k-space data. A specially designed co-training loss is designed to guide the two networks to capture the inherent features and representations of the MR image.
• Score: 9.16860702327751
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the successful application of deep learning in magnetic resonance imaging, parallel imaging techniques based on neural networks have attracted wide attentions. However, without high-quality fully sampled datasets for training, the performance of these methods tends to be limited. To address this issue, this paper proposes a physics based unsupervised contrastive representation learning (PARCEL) method to speed up parallel MR imaging. Specifically, PARCEL has three key ingredients to achieve direct deep learning from the undersampled k-space data. Namely, a parallel framework has been developed by learning two branches of model-based networks unrolled with the conjugate gradient algorithm; Augmented undersampled k-space data randomly drawn from the obtained k-space data are used to help the parallel network to capture the detailed information. A specially designed co-training loss is designed to guide the two networks to capture the inherent features and representations of the-to-be-reconstructed MR image. The proposed method has been evaluated on in vivo datasets and compared to five state-of-the-art methods, whose results show PARCEL is able to learn useful representations for more accurate MR reconstructions without the reliance on the fully-sampled datasets.

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