MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation

• URL: http://arxiv.org/abs/2209.00462v2
• Date: Sun, 26 Nov 2023 07:24:04 GMT
• Title: MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation
• Authors: Nitzan Avidan and Moti Freiman
• Abstract summary: High-quality reconstruction of MRI images from under-sampled kspace' data is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. This paper introduces MA-RECON', an innovative mask-aware deep neural network (DNN) architecture and associated training method. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem.
• Score: 3.0821115746307672
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-quality reconstruction of MRI images from under-sampled `k-space' data, which is in the Fourier domain, is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. Over recent years, a wealth of deep neural network (DNN) methods have emerged, aiming to tackle the complex, ill-posed inverse problem linked to this process. However, their instability against variations in the acquisition process and anatomical distribution exposes a deficiency in the generalization of relevant physical models within these DNN architectures. The goal of our work is to enhance the generalization capabilities of DNN methods for k-space interpolation by introducing `MA-RECON', an innovative mask-aware DNN architecture and associated training method. Unlike preceding approaches, our `MA-RECON' architecture encodes not only the observed data but also the under-sampling mask within the model structure. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem. Therefore, effectively represents the associated inverse problem, akin to the classical compressed sensing approach. The benefits of our MA-RECON approach were affirmed through rigorous testing with the widely accessible fastMRI dataset. Compared to standard DNN methods and DNNs trained with under-sampling mask augmentation, our approach demonstrated superior generalization capabilities. This resulted in a considerable improvement in robustness against variations in both the acquisition process and anatomical distribution, especially in regions with pathology. In conclusion, our mask-aware strategy holds promise for enhancing the generalization capacity and robustness of DNN-based methodologies for MRI reconstruction from undersampled k-space data.

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