讲座题目：Deep Learning Based CT Image Reconstruction From Incomplete Data

讲座时间：2021年12月03日，星期五，下午：2:30-3:30

腾讯会议 ID：438 406 434

腾讯会议密码：1234

讲座人：张小群教授，上海交通大学

讲座简介：

Image reconstruction from down-sampled and corrupted measurements, such as fast MRI and low dose CT, is mathematically ill-posed inverse problem. Deep neural network (DNN) has been becoming a prominent tool in the recent development of medical image reconstruction methods. In this talk, I will introduce two work on incorporating classical image reconstruction method and deep learning methods. In the first work, in order to address the intractable inversion of general inverse problems, we propose to train a network to refine intermediate images from classical reconstruction procedure to the ground truth, i.e. the intermediate images that satisfy the data consistence will be fed into some chosen denoising networks or generative networks for denoising and removing artifact in each iterative stage. In the second work, we proposed a multi-scale DNN for sparse view CT reconstruction, which directly learns an interpolation scheme to predict the complete set of 2D Fourier coefficients in Cartesian coordinates from the given measurements in polar coordinates. In the second work, we proposed an unsupervised deep learning method for LDCT image reconstruction, which does not require any external training data. The proposed method is built on a re-parameterization technique for Bayesian inference via deep network with random weights, combined with additional total variational~(TV) regularization. The experiments on both sparse CT and low dose CT problem show that the proposed method provided state-of-the-art performance.

讲座人简介：

    张小群, 上海交通大学自然科学研究院和数学科学学院特聘教授。主要研究方向：图像科学、医学图像处理、数据科学等问题中的数学模型与计算方法。现任Inverse Problems and Imaging 杂志编委，中国工业与应用数学大数据与人工智能专委会、数学与医学交叉学科专业委员会委员。

组织者：

贾骏雄 西安交通大学副教授

孟德宇 西安交通大学教授

赵熙乐 电子科技大学教授

具体会议信息：

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会议主题：贾骏雄预定的会议

会议时间：2021/12/03 14:30-16:00 (GMT+08:00) 中国标准时间 - 香港

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腾讯会议：438-406-434

会议密码：1234