Low Dose CT Denoising research aims to reduce the risks of radiation exposure to patients. Recently researchers have used deep learning to denoise low dose CT images with promising results. However, approaches that use mean-squared-error (MSE) tend to over smooth the image resulting in loss of fine structural details in low contrast regions of the image. These regions are often crucial for diagnosis and must be preserved in order for Low dose CT to be used effectively in practice. In this work we use a cascade of two neural networks, the first of which aims to reconstruct normal dose CT from low dose CT by minimizing perceptual loss, and the second which predicts the difference between the ground truth and prediction from the perceptual loss network. We show that our method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion
University of Florida（佛罗里达大学）
随着公众对医学成像中累积的辐射暴露引起的潜在癌症风险和健康危害的大量关注，减少基于X射线的医学成像（诸如计算机断层扫描灌注（CTP））中的辐射剂量已经引起了重大的研究兴趣。在本文中，我们采用基于深度卷积神经网络（CNN）的方法，并引入平滑密集卷积神经网络（SDCNet）来从低剂量图像恢复高剂量质量CTP图像。SDCNet由通过跳跃连接级联的子网络块组成，以从成对的低/高剂量CT扫描中推断噪声（差异）。SDCNet可以有效地去除实际低剂量CT扫描中的噪声，提高医学图像的质量。我们评估了数千个CT灌注帧的重建图像去噪和灌注图量化，包括脑血流量（CBF）和脑血容量（CBV）的建议架构。SDCNet在视觉和定量结果方面都实现了高性能，具有良好的计算效率，与最先进的方法相比毫不逊色。\textit{The code is available at \url{https://github.com/cswin/RC-Nets}}。
Low-dose CT Denoising with Language-engaged Dual-space Alignment
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute（伦斯勒理工学院生物技术与跨学科研究中心）
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
传统的基于模型的图像重建（MBIR）方法将前向模型和噪声模型与简单的对象先验相结合。最近深度学习方法在图像重建中的应用提供了一种成功的数据驱动方法，可以解决在测量欠采样或各种类型噪声的情况下重建图像时面临的挑战。在这项工作中，我们提出了一个混合监督-无监督学习框架的X射线计算机断层扫描（CT）图像重建。所提出的学习公式利用稀疏或无监督学习的先验知识和神经网络重构器来模拟定点迭代过程。每个建议的训练块由确定性MBIR求解器和神经网络组成。信息并行地流过这两个重构器，然后被最佳地组合，并且多个这样的块被级联以形成重构流水线。我们证明了这种学习的混合模型在训练数据有限的情况下用于低剂量CT图像重建的有效性，其中我们使用NIH AAPM Mayo Clinic低剂量CT Grand Challenge数据集进行训练和测试。在我们的实验中，我们研究了有监督的深度网络重构器和基于稀疏表示（无监督）的学习或分析先验的组合。我们的研究结果表明，所提出的框架相比，最近的重建方法有前途的性能。
Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning
University of Michigan Shanghai Jiao Tong University Joint Institute（密歇根大学上海交通大学联合学院）；Shanghai Jiao Tong University（上海交通大学）；Amazon Inc.（亚马逊公司）；Michigan State University（密歇根州立大学）
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）；São Carlos School of Engineering, University of São Paulo（圣保罗大学圣卡洛斯工程学院）；Ribeirão Preto School of Medicine, University of São Paulo（圣保罗大学里贝朗普雷图医学院）
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms
School of Computer Science, Sichuan University（四川大学计算机学院）；School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising
College of Life Science and Technology, Huazhong University of Science and Technology（华中科技大学生命科学技术学院）；New York University Shanghai（上海纽约大学）；School of Life Sciences and Biotechnology, Shanghai Jiao Tong University（上海交通大学生命科学技术学院）
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（脑启发智能科学与技术研究院，复旦大学）；Department of Biomedical Engineering, Yale University（生物医学工程系，耶鲁大学）；Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute（生物医学成像中心，生物技术与跨学科研究所在生物医学工程系，罗切斯特理工学院）；Institute of Science and Technology for Brain-inspired Intelligence, MOE Frontiers Center for Brain Science and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education), Fudan University（脑启发智能科学与技术研究院，教育部脑科学前沿中心和计算神经科学与脑启发智能重点实验室，复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与脑启发技术中心）
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；University of Innsbruck（因斯布鲁克大学）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院）；Technical University of Berlin（柏林工业大学）；German Cancer Research Center（德国癌症研究中心）
