压缩感知与深度学习在不同序列肩部磁共振成像中的联合应用。
Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.
机构信息
From the Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University.
Department of Radiology, Kumamoto University Hospital, Kumamoto.
出版信息
J Comput Assist Tomogr. 2023;47(2):277-283. doi: 10.1097/RCT.0000000000001418. Epub 2023 Mar 9.
OBJECTIVE
For compressed sensing (CS) to become widely used in routine magnetic resonance imaging (MRI), it is essential to improve image quality. This study aimed to evaluate the usefulness of combining CS and deep learning-based reconstruction (DLR) for various sequences in shoulder MRI.
METHODS
This retrospective study included 37 consecutive patients who underwent undersampled shoulder MRIs, including T1-weighted (T1WI), T2-weighted (T2WI), and fat-saturation T2-weighted (FS-T2WI) images. Images were reconstructed using the conventional wavelet-based denoising method (wavelet method) and a combination of wavelet and DLR-based denoising methods (hybrid-DLR method) for each sequence. The signal-to-noise ratio and contrast-to-noise ratio of the bone, muscle, and fat and the full width at half maximum of the shoulder joint were compared between the 2 image types. In addition, 2 board-certified radiologists scored the image noise, contrast, sharpness, artifacts, and overall image quality of the 2 image types on a 4-point scale.
RESULTS
The signal-to-noise ratios and contrast-to-noise ratios of the bone, muscle, and fat in T1WI, T2WI, and FS-T2WI obtained from the hybrid-DLR method were significantly higher than those of the conventional wavelet method ( P < 0.001). However, there were no significant differences in the full width at half maximum of the shoulder joint in any of the sequences ( P > 0.05). Furthermore, in all sequences, the mean scores of the image noise, sharpness, artifacts, and overall image quality were significantly higher in the hybrid-DLR method than in the wavelet method ( P < 0.001), but there were no significant differences in contrast among the sequences ( P > 0.05).
CONCLUSIONS
The DLR denoising method can improve the image quality of CS in T1-weighted images, T2-weighted images, and fat-saturation T2-weighted images of the shoulder compared with the wavelet denoising method alone.
目的
为了使压缩感知（CS）在磁共振成像（MRI）中得到广泛应用，提高图像质量至关重要。本研究旨在评估 CS 与基于深度学习的重建（DLR）相结合在各种肩部 MRI 序列中的应用价值。
方法
本回顾性研究纳入了 37 例连续接受欠采样肩部 MRI 检查的患者，包括 T1 加权（T1WI）、T2 加权（T2WI）和脂肪饱和 T2 加权（FS-T2WI）图像。对每个序列的图像分别采用传统的基于小波的去噪方法（小波方法）和基于小波和 DLR 的联合去噪方法（混合-DLR 方法）进行重建。比较两种图像类型的骨、肌肉和脂肪的信噪比和对比噪声比以及肩关节的全宽半高值。此外，两位经过认证的放射科医生对两种图像类型的图像噪声、对比度、锐利度、伪影和整体图像质量进行 4 分制评分。
结果
混合-DLR 方法获得的 T1WI、T2WI 和 FS-T2WI 的骨、肌肉和脂肪的信噪比和对比噪声比均显著高于传统的小波方法（P<0.001）。然而，在任何序列中，肩关节的全宽半高值均无显著差异（P>0.05）。此外，在所有序列中，混合-DLR 方法的图像噪声、锐利度、伪影和整体图像质量的平均评分均显著高于小波方法（P<0.001），但序列之间的对比度无显著差异（P>0.05）。
结论
与单独的小波去噪方法相比，DLR 去噪方法可以提高 CS 在肩部 T1WI、T2WI 和脂肪饱和 T2WI 中的图像质量。
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