Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction
State Key Laboratory of Advanced Medical Materials and Devices（先进医用材料与器件国家重点实验室）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
Zhentao Liu, Yu Fang, Changjian Li, Han Wu, Yuan Liu, Dinggang Shen, Zhiming Cui
Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image, leading to considerable radiation exposure. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. While recent advances, including deep learning and neural rendering algorithms, have made strides in this area, these methods either produce unsatisfactory results or suffer from time inefficiency of individual optimization. In this paper, we introduce a novel geometry-aware encoder-decoder framework to solve this problem. Our framework starts by encoding multi-view 2D features from various 2D X-ray projections with a 2D CNN encoder. Leveraging the geometry of CBCT scanning, it then back-projects the multi-view 2D features into the 3D space to formulate a comprehensive volumetric feature map, followed by a 3D CNN decoder to recover 3D CBCT image. Importantly, our approach respects the geometric relationship between 3D CBCT image and its 2D X-ray projections during feature back projection stage, and enjoys the prior knowledge learned from the data population. This ensures its adaptability in dealing with extremly sparse view inputs without individual training, such as scenarios with only 5 or 10 X-ray projections. Extensive evaluations on two simulated datasets and one real-world dataset demonstrate exceptional reconstruction quality and time efficiency of our method.
Intensity Field Decomposition for Tissue-Guided Neural Tomography
School of Stomatology, Wuhan University（武汉大学口腔医学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；Pazhou Laboratory（琶洲实验室）
ShanghaiTech University（上海科技大学）；The University of Edinburgh（爱丁堡大学）；The University of Hong Kong（香港大学）；Texas A&M University（得克萨斯农工大学）
Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging. Several research works have been proposed to reconstruct high-quality CBCT images from sparse-view 2D projections, but the current state-of-the-arts suffer from artifacts and the lack of fine details. In this paper, we propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields, where we have invented a novel view augmentation strategy to overcome the challenges int…
This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction (Cone Beam Computed Tomography) that requires no external training data. Specifically, the desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network. We synthesize projections discretely and train the network by minimizing the error between real and synthesized projections. A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details. This encoder outper…
3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning
School of Biomedical Engineering \& State Key Laboratory of Advanced Medical Materials；Devices, ShanghaiTech University, Shanghai, China；National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China；School of Electronic Information；Communications, Huazhong University of Science；Department of Computer Science \& Engineering, Texas A\&M University, USA
DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Cyber Science and Engineering, Sichuan University（网络科学与工程学院，四川大学）
Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model
School of Information Engineering, Nanchang University（南昌大学信息工程学院）；School of Mathematics and Computer Sciences, Nanchang University（南昌大学数学与计算机科学学院）；School of Biomedical Engineering, Sun Yat-sen University（中山大学生物医学工程学院）；Laboratory of Image Science and Technology, School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University（东南大学计算机科学与工程学院图像科学与技术实验室，以及教育部新一代人工智能技术及其交叉应用重点实验室）
During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems. Due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality greatly. To reduce the artifacts, many metal artifact reduction algorithms have been developed and metal inpainting in projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections and the inpainted image is obtained by removing the noise in conditional resampling process. The result implies…
Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
Swiss Data Science Center (SDSC) in Paul Scherrer Institute (PSI)（瑞士数据科学中心（SDSC）在保罗·舍勒研究所（PSI））；Swiss Data Science Center (SDSC) and ETH Zurich（瑞士数据科学中心（SDSC）和苏黎世联邦理工学院）；Swiss Data Science Center (SDSC) and Paul Scherrer Institute (PSI)（瑞士数据科学中心（SDSC）和保罗·舍勒研究所（PSI））
RePrior: Measurement-Reconciled Prior Correction for Sparse-View CBCT Reconstruction
University of Central Florida（中佛罗里达大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tampere University（坦佩雷大学）；Ellis Institute Finland（芬兰埃利斯研究所）
This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment interventional instruments and thus guides the reconstruction task. The prior scans are misaligned by up to +-5deg in-plane during training. Experiments show that the proposed model, Dual Branch Prior-SegNet, significantly outperforms any other evaluated model by >2.8dB PSNR. It also stays robust wrt. rotations of up to +-5.5deg.
Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction
Department of Electrical Engineering & Computer Science, University of Michigan（电气工程与计算机科学系，密歇根大学）；Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）
RSTAR4D: Rotational Streak Artifact Reduction in 4D CBCT using a Separable 4D CNN
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属胸科医院）；Jiangsu First-Imaging Tech.（江苏首影科技有限公司）；Research Center of Augmented Intelligence, Zhejiang Lab（之江实验室增强智能研究中心）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction
Huazhong University of Science and Technology（华中科技大学）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southeast University（东南大学）；United Imaging Healthcare Limited Company（联影医疗科技股份有限公司）
In several image acquisition and processing steps of X-ray radiography, knowledge of the existence of metal implants and their exact position is highly beneficial (e.g. dose regulation, image contrast adjustment). Another application which would benefit from an accurate metal segmentation is cone beam computed tomography (CBCT) which is based on 2D X-ray projections. Due to the high attenuation of metals, severe artifacts occur in the 3D X-ray acquisitions. The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact avoidance and reduction algorithms. Since t…
NeRF-based CBCT Reconstruction needs Normalization and Initialization
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China (USTC), Hefei, 230026, China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou, 215123, China Computer Aided Medical Procedures (CAMP), TU Munich, 80333, Germany Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou, 215123, China State Key Laboratory of Precision \& Intelligent Chemistry, USTC, Hefei, China Munich Center for Machine Learning (MCML), Munich, Germany
4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images
State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学先进医用材料与器件国家重点实验室）；School of Computing, Australian National University（澳大利亚国立大学计算学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. In mammography, measurement of breast density, dose estimation, and differentiation between cysts and solid tumours are example applications requiring accurate data on tissue attenuation. Published attenuation data are, however, sparse and cover a relatively wide range. To supplement available data we have previously measured the attenuation of cyst fluid and solid lesions using photon-counting spectral mammography. The present study aims to measure the attenuation of normal adipose and glandu…
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Hong Kong University of Science and Technology（香港科技大学）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Samsung Research China - Beijing (SRC-B)（三星中国研究院北京分部）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区）；SAMSUNG Electronics（三星电子）
Swarthmore College（斯沃斯莫尔学院）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Renewable Energy Laboratory（国家可再生能源实验室）
Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation angles allows for improved reconstruction. However, taking more measurements increases the x-ray dose and may cause sample damage. Deep neural networks have been used to tra…
The importance of regularization has been well established in image reconstruction -- which is the computational inversion of imaging forward model -- with applications including deconvolution for microscopy, tomographic reconstruction, magnetic resonance imaging, and so on. Originally, the primary role of the regularization was to stabilize the computational inversion of the imaging forward model against noise. However, a recent framework pioneered by Donoho and others, known as compressive sensing, brought the role of regularization beyond the stabilization of inversion. It established a pos…
Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into …
