Kojima Shinya, Ito Toshimune, Hayashi Tatsuya
Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Itabashi-ku, Tokyo, Japan.
J Med Phys. 2022 Oct-Dec;47(4):387-393. doi: 10.4103/jmp.jmp_71_22. Epub 2023 Jan 10.
To reduce noise for low-field magnetic resonance imaging (MRI) using Noise2Void (N2V) and to demonstrate the N2V validity. N2V is one of the denoising convolutional neural network methods that allows the training of a model without a noiseless clean image. In this study, a kiwi fruit was scanned using a 0.35 Tesla MRI system, and the image qualities at pre- and postdenoising were evaluated. Structural similarity (SSIM), signal-to-noise ratio (SNR), and contrast ratio (CR) were measured, and visual assessment of noise and sharpness was observed. Both SSIM and SNR were significantly improved using N2V ( < 0.05). CR was unchanged between pre- and postdenoising images. The results of visual assessment for noise revealed higher scores in postdenoising images than that in predenoising images. The sharpness scores of postdenoising images were high when SNR was low. N2V provides effective noise reduction and is a useful denoising technique in low-field MRI.
使用噪声到空白（N2V）方法降低低场磁共振成像（MRI）的噪声，并验证N2V的有效性。N2V是一种去噪卷积神经网络方法，它允许在没有无噪声清晰图像的情况下训练模型。在本研究中，使用0.35特斯拉MRI系统对一个奇异果进行扫描，并评估去噪前后的图像质量。测量了结构相似性（SSIM）、信噪比（SNR）和对比度比（CR），并观察了噪声和锐度的视觉评估。使用N2V后，SSIM和SNR均显著提高（<0.05）。去噪前后图像的CR没有变化。噪声的视觉评估结果显示，去噪后图像的得分高于去噪前图像。当SNR较低时，去噪后图像的锐度得分较高。N2V提供了有效的降噪效果，是低场MRI中一种有用的去噪技术。
