Three-dimensional (3D) X-ray imaging techniques like tomography and confocal microscopy are crucial for academic and industrial applications. These approaches access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for some applications, such as surgical guidance in medical applications. Alternatives to obtaining 3D information when scanning is not possible are X-ray stereoscopy and multi-projection imaging. However, these approaches suffer from limited volumetric information as they only acquire a small number of views or projections compared to traditional 3D scanning techniques. Here, we present ONIX (Optimized Neural Implicit X-ray imaging), a deep-learning algorithm capable of retrieving 3D objects with arbitrary large resolution from only a set of sparse projections. ONIX, although it does not have access to any volumetric information, outperforms current 3D reconstruction approaches because it includes the physics of image formation with X-rays, and it generalizes across different experiments over similar samples to overcome the limited volumetric information provided by sparse views. We demonstrate the capabilities of ONIX compared to state-of-the-art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. We anticipate that ONIX will become a crucial tool for the X-ray community by i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging, and ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging in medical applications.
An evaluation framework for sparse 4D (3D + time) imaging reconstruction via bootstrapped cross-validation
Synchrotron Radiation Research and NanoLund, Lund University（隆德大学同步辐射研究与纳米隆德中心）；Medical Radiation Physics, Lund University（隆德大学医学放射物理）；University College London（伦敦大学学院）
NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands（数学与计算机科学系，埃因霍温理工大学，埃因霍温，荷兰）；Philips Healthcare, Best, The Netherlands（飞利浦医疗，贝斯特，荷兰）；Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands（电子工程系，埃因霍温理工大学，荷兰）
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。
AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
Department of Mathematics & Computer Science, Eindhoven University of Technology（数学与计算机科学系，埃因霍温理工大学）；Philips Healthcare（飞利普医疗）；Department of Electrical Engineering, Eindhoven University of Technology（电子工程系，埃因霍温理工大学）；ASML（ASML公司）
DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Suzhou Institute of Intelligent Computing Technology, CAS（中国科学院苏州智能计算技术研究院）；Shenzhen Institutes of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；Beijing Information Science and Technology University（北京信息科技大学）；University of Science and Technology of China（中国科学技术大学）
MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction
Xiangtan University（湘潭大学）；Zhejiang Normal University（浙江师范大学）；The University of Liverpool（利物浦大学）；University of Strathclyde（斯特拉斯克莱德大学）；Hunan National Applied Mathematics Center（湖南国家应用数学中心）
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（中国科学技术大学生命科学与医学部生物医学工程学院）；Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州研究院医学影像、机器人、分析计算与学习中心）
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）
