Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition

摘要

Typical Magnetic Resonance Imaging (MRI) scan may take 20 to 60 minutes. Reducing MRI scan time isbeneficial for both patient experience and cost considerations. Accelerated MRI scan may be achieved byacquiring less amount of k-space data (down-sampling in the k-space). However, this leads to lower resolutionand aliasing artifacts for the reconstructed images. There are many existing approaches for attempting toreconstruct high-quality images from down-sampled k-space data, with varying complexity and performance.In recent years, deep-learning approaches have been proposed for this task, and promising results have beenreported. Still, the problem remains challenging especially because of the high fidelity requirement in mostmedical applications employing reconstructed MRI images. In this work, we propose a deep-learning approach,aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use ConvolutionalNeural Network (CNN) to learn the differences between the aliased images and the original images, employinga U-Net-like architecture. Further, a micro-architecture termed Residual Dense Block (RDB) is introducedfor learning a better feature representation than the plain U-Net. Considering the peculiarity of the downsampledk-space data, we introduce a new term to the loss function in learning, which effectively employs thegiven k-space data during training to provide additional regularization on the update of the network weights. Toevaluate the proposed approach, we compare it with other state-of-the-art methods. In both visual inspection andevaluation using standard metrics, the proposed approach is able to deliver improved performance, demonstratingits potential for providing an effective solution.