论文标题
使用部分卷积的金属伪像还原的投影介绍
Projection Inpainting Using Partial Convolution for Metal Artifact Reduction
论文作者
论文摘要
在计算机断层扫描中，由于患者体内存在金属植入物，重建的图像将遭受金属伪像。为了减少金属伪像，通常在投影图像中除去金属。因此，金属损坏的投射区域需要被覆盖。对于深度学习灌溉方法，例如，卷积神经网络（CNN）被广泛使用，例如U-NET。但是，此类CNN在有效和损坏的像素值上都使用卷积过滤器响应，从而导致图像质量不令人满意。在这项工作中，部分卷积被用于投影介绍，这仅依赖于有效的像素值。比较具有部分卷积和常规卷积的U-NET，以减少金属伪影。我们的实验表明，具有部分卷积的U-NET能够比传统卷积更好地注入金属损坏的区域。
In computer tomography, due to the presence of metal implants in the patient body, reconstructed images will suffer from metal artifacts. In order to reduce metal artifacts, metals are typically removed in projection images. Therefore, the metal corrupted projection areas need to be inpainted. For deep learning inpainting methods, convolutional neural networks (CNNs) are widely used, for example, the U-Net. However, such CNNs use convolutional filter responses on both valid and corrupted pixel values, resulting in unsatisfactory image quality. In this work, partial convolution is applied for projection inpainting, which only relies on valid pixels values. The U-Net with partial convolution and conventional convolution are compared for metal artifact reduction. Our experiments demonstrate that the U-Net with partial convolution is able to inpaint the metal corrupted areas better than that with conventional convolution.
