Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
Institute of Science and Technology for Brain-inspired Intelligence（脑启发智能科学与技术研究院）；Fudan University（复旦大学）；School of Biomedical Engineering（生物医学工程学院）；Southern Medical University（南方医科大学）；School of Computer Science（计算机科学学院）；Xi’an Jiaotong University（西安交通大学）；School of Life Science and Technology（生命科学与技术学院）；MOE Frontiers Center for Brain Science（教育部脑科学前沿中心）；Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education)（教育部计算神经科学与脑启发智能重点实验室）；State Key Laboratory of Brain Function and Disorders（脑功能与疾病国家重点实验室）
The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previous efforts heavily rely on paired data to improve the generalization performance and robustness through collecting either diverse CT data for re-training or a few test data for fine-tuning. Recently, diffusion models have shown promising and generalizable performance in low-dose CT (LDCT) reconstruction, however, they may produce unrealistic structures due to the CT image noise deviating from Gaussian distribution and imprecise prior information from the guidance of noisy LDCT images. In this paper, we propose a noise-inspired diffusion model for generalizable LDCT reconstruction, termed NEED, which tailors diffusion models for noise characteristics of each domain. First, we propose a novel shifted Poisson diffusion model to denoise projection data, which aligns the diffusion process with the noise model in pre-log LDCT projections. Second, we devise a doubly guided diffusion model to refine reconstructed images, which leverages LDCT images and initial reconstructions to more accurately locate prior information and enhance reconstruction fidelity. By cascading these two diffusion models for dual-domain reconstruction, our NEED requires only normal-dose data for training and can be effectively extended to various unseen dose levels during testing via a time step matching strategy. Extensive qualitative, quantitative, and segmentation-based evaluations on two datasets demonstrate that our NEED consistently outperforms state-of-the-art methods in reconstruction and generalization performance. Source code is made available at https://github.com/qgao21/NEED.
FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University（脑启发智能科学技术研究所和脑科学前沿中心，复旦大学）；Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University（智能信息处理上海市重点实验室，复旦大学计算机科学学院）；School of Cyber Science and Engineering, Sichuan University（四川大学网络科学与工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；Affiliated Hospital of Xiangnan University（湘南大学附属医院）；Jiangxi Cancer Hospital（江西省肿瘤医院）；Chenzhou Third People's Hospital（郴州第三人民医院）
DEFUSE: Generalizable Backdoor Defense for Self-Supervised Encoders with Generative Priors
Ant Group（蚂蚁集团）；Purple Mountain Laboratories（紫金山实验室）；City University of Hong Kong（香港城市大学）；Beijing Institute of Technology（北京理工大学）；Alibaba Group（阿里巴巴集团）
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this paper, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (Sino…
We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks to generate Pareto optimal dose distributions. The mean squared error (MSE) loss, dose volume histogram (DVH) loss, and adversarial (ADV) loss were used to train 4 instances of the neural network model: 1) MSE, 2) MSE+ADV, 3) MSE+DVH, and 4) MSE+DVH+ADV. 70 prostate patients were acquired, and the dose influence arrays were calculated for each patient. 1200 Pareto surface plans per patient were gene…
Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
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）；Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司研发部）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Peking University Cancer Hospital（北京大学肿瘤医院）
Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We developed a semantic physics-based data augmentation method for segmenting esophagus in both planning CT (pCT) and cone-beam CT (CBCT) using 3D convolutional neural networks. 191 cases with their pCT and CBCTs from four independent datasets were used to train a modified 3D-Unet architecture with a multi-objective loss function specifically designed for soft-tissue organs such as esophagus. Scatter artifacts and noise were ex…
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；School of Integrated Circuits and Electronics, Beijing Institute of Technology（北京理工大学集成电路与电子学院）
University of Texas Southwestern Medical Centre（德克萨斯大学西南医学中心）
Low Dose Computed Tomography (LDCT) is clinically desirable due to the reduced radiation to patients. However, the quality of LDCT images is often sub-optimal because of the inevitable strong quantum noise. Inspired by their unprecedent success in computer vision, deep learning (DL)-based techniques have been used for LDCT denoising. Despite the promising noise removal ability of DL models, people have observed that the resolution of the DL-denoised images is compromised, decreasing their clinical value. Aiming at relieving this problem, in this work, we developed a more effective denoiser by …
University of São Paulo（圣保罗大学）；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）
Digital breast tomosynthesis (DBT) exams should utilize the lowest possible radiation dose while maintaining sufficiently good image quality for accurate medical diagnosis. In this work, we propose a convolution neural network (CNN) to restore low-dose (LD) DBT projections to achieve an image quality equivalent to a standard full-dose (FD) acquisition. The proposed network architecture benefits from priors in terms of layers that were inspired by traditional model-based (MB) restoration methods, considering a model-based deep learning approach, where the network is trained to operate in the va…
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
School of Biomedical Engineering, Southern Medical University（生物医学工程学院，南方医科大学）；School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南芳PET中心，南方医科大学南芳医院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University（广东省医学图像处理重点实验室，南方医科大学）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University（广东省医学影像与诊断技术工程实验室，南方医科大学）；Pazhou Lab（琶洲实验室）
Low-dose CT (LDCT) imaging is desirable in many clinical applications to reduce X-ray radiation dose to patients. Inspired by deep learning (DL), a recent promising direction of model-based iterative reconstruction (MBIR) methods for LDCT is via optimization-unrolling DL-regularized image reconstruction, where pre-defined image prior is replaced by learnable data-adaptive prior. However, LDCT is clinically multilevel, since clinical scans have different noise levels that depend of scanning site, patient size, and clinical task. Therefore, this work aims to develop an adaptive-hyper-parameter D…
A deep dictionary network-based foundation model for ultra-low-dose CT denoising
Yanshan University（燕山大学）；Capital Medical University（首都医科大学）；Beijing Friendship Hospital（北京友谊医院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading
School of Electronic and Information Engineering, Guangxi Normal University（广西师范大学电子与信息工程学院）；School of Computer Science and Engineering, Guangxi Normal University（广西师范大学计算机科学与工程学院）；University of Cambridge（剑桥大学）
