A bidirectional registration neural network for cardiac motion tracking using cine MRI images

Lu, Junlin; Jin, Renchao; Wang, Manyang; Song, Enmin; Ma, Guangzhi

doi:10.1016/j.compbiomed.2023.107001

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…Many network structures have been developed for the removal of MRI motion artifacts. The network frameworks include residual networks (ResNet) (Zhang et al 2019, Pawar et al 2022, recurrent neural network (RNN) (Qin et al 2019, Lu et al 2023, U-Net network (Aghabiglou andEksioglu 2021, Al-masni et al 2022), and so on. Clinical applications have shown that these deep learning methods are effective on MRI obtained from different pulse sequences (Chen et al 2023).…”

Section: Introductionmentioning

confidence: 99%

A cardiac MRI motion artifact reduction method based on edge enhancement network

Jiang,

Zhang,

et al. 2024

Phys. Med. Biol.

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Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Then, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion Transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index Measure(SSIM) compared to state-of-art methods. The de-artifact images have clear texture edges.

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