论文标题
由自动光谱归一化的Markovian Patch GAN驱动的非局部神经网络的噪声训练，低剂量CT denoising
Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
论文作者
论文摘要
在医学实践中使用计算机断层扫描（CT）成像的爆炸性兴起激起了公众对患者相关辐射剂量的关注。但是，减少辐射剂量会导致噪声和人工制品增加，从而不利地降低了扫描的可解释性。因此，提高低剂量CT诊断性能的先进图像重建算法是研究人员的主要关注点，这是由于问题的不良性而具有挑战性的。最近，基于深度学习的技术已成为低剂量CT（LDCT）降级的主要方法。但是，仍然存在一些常见的瓶颈，这阻碍了基于深度学习的技术提供最佳性能。在这项研究中，我们试图通过三个新型积聚来减轻这些问题。首先，我们提出了一个新颖的卷积模块，这是第一次利用CT图像的邻里相似性来降低任务的尝试。我们提议的模块有助于通过大幅度的边缘提高脱诺。接下来，我们朝着CT噪声的非平稳性问题发展，并引入了新的噪声意识到LDCT DeNoising的均方误差损失。此外，上面提到的损失还有助于减轻使用图像补丁训练CT DeNo网络所需的费力努力。最后，我们提出了用于CT deoing任务的新型歧视函数。传统的香草歧视者倾向于忽略精美的结构细节，并专注于全球协议。我们提出的歧视者利用自我注意力和像素甘斯的gan来恢复LDCT图像的诊断质量。我们的方法在2016年NIH-AAPM-Mayo诊所低剂量CT Grand Challenge的公开数据集上进行了验证，其表现要比现有的最先进的方法要好得多。
The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. However, reducing the radiation dose leads to increased noise and artifacts, which adversely degrades the scan's interpretability. Consequently, an advanced image reconstruction algorithm to improve the diagnostic performance of low dose ct arose as the primary concern among the researchers, which is challenging due to the ill-posedness of the problem. In recent times, the deep learning-based technique has emerged as a dominant method for low dose CT(LDCT) denoising. However, some common bottleneck still exists, which hinders deep learning-based techniques from furnishing the best performance. In this study, we attempted to mitigate these problems with three novel accretions. First, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Our proposed module assisted in boosting the denoising by a significant margin. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising. Moreover, the loss mentioned above also assisted to alleviate the laborious effort required while training CT denoising network using image patches. Lastly, we propose a novel discriminator function for CT denoising tasks. The conventional vanilla discriminator tends to overlook the fine structural details and focus on the global agreement. Our proposed discriminator leverage self-attention and pixel-wise GANs for restoring the diagnostic quality of LDCT images. Our method validated on a publicly available dataset of the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge performed remarkably better than the existing state of the art method.
