Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2024-05-02 eCollection Date: 2024-01-01 DOI:10.1155/2024/8972980
Md Biddut Hossain, Rupali Kiran Shinde, Shariar Md Imtiaz, F M Fahmid Hossain, Seok-Hee Jeon, Ki-Chul Kwon, Nam Kim
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Abstract

We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar.

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用斯温变换器和 Unet 架构纠正磁共振图像重建中的运动伪影
我们提出了一种基于深度学习的方法,它能纠正运动伪影,从而加速磁共振图像的数据采集和重建。这个名为 "Swin 网络运动伪影校正"(MACS-Net)的新模型使用 Swin 变换器层作为基本模块,Unet 架构作为神经网络骨干。在编码过程中,我们采用带有移位窗口的分层变换器来提取多尺度上下文特征。在基于 Swin 变换器的解码层中,我们采用了一种新的双重上采样技术来提高特征图的空间分辨率。原始磁共振成像数据集用于网络训练和测试;数据包含各种运动伪影和相同受试者的地面实况图像。使用两种类型的运动,将结果与六种最先进的磁共振成像运动校正方法进行了比较。当运动时间较短时(5 秒内),该方法将平均归一化均方根误差(NRMSE)从 45.25% 降低到 17.51%,将平均结构相似性指数(SSIM)从 79.43% 提高到 91.72%,将峰值信噪比(PSNR)从 18.24 dB 提高到 26.57 dB。同样,当运动时间从 5 秒延长到 10 秒时,我们的方法将平均 NRMSE 从 60.30% 降低到 21.04%,将平均 SSIM 从 33.86% 提高到 90.33%,将 PSNR 从 15.64 dB 提高到 24.99 dB。校正图像和无运动大脑数据的解剖结构相似。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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