论文标题
Transem：基于剩余的Swin-Transformer基于正规PET图像重建
TransEM:Residual Swin-Transformer based regularized PET image reconstruction
论文作者
论文摘要
正电子发射断层扫描（PET）图像重建是一个不良的反问题，由于收到的计数有限而受到高水平的噪声。最近，深层神经网络特别是卷积神经网络（CNN）已成功应用于PET图像重建。但是，卷积操作员的局部特征可能会限制通过当前基于CNN的PET图像重建方法获得的图像质量。在本文中，我们提出了一个基于Swin-Transformer的剩余正规器（RSTR），以将正则化纳入迭代重建框架中。具体而言，首先采用卷积层提取浅特征，然后由Swin-Transformer层完成深度提取。最后，深层和浅的特征都与残余操作和另一个卷积层融合在一起。对现实的3D大脑模拟低计数数据的验证表明，我们所提出的方法在定性和定量测量中都优于最先进的方法。
Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural networks especially convolutional neural networks(CNN) have been successfully applied to PET image reconstruction. However, the local characteristics of the convolution operator potentially limit the image quality obtained by current CNN-based PET image reconstruction methods. In this paper, we propose a residual swin-transformer based regularizer(RSTR) to incorporate regularization into the iterative reconstruction framework. Specifically, a convolution layer is firstly adopted to extract shallow features, then the deep feature extraction is accomplished by the swin-transformer layer. At last, both deep and shallow features are fused with a residual operation and another convolution layer. Validations on the realistic 3D brain simulated low-count data show that our proposed method outperforms the state-of-the-art methods in both qualitative and quantitative measures.
