Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is a particularly attractive research to promote the applications of DECT in a wide range of imaging areas. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from dual limited-angle projection data, which enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses. We first study the characteristics of image artifacts under dual quarter scans scheme, and find that the directional limited-angle artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are orthogonal. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy largely reduces the limited-angle artifacts and preserves the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method.
Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI
Massachusetts Institute of Technology（麻省理工学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Tsinghua University（清华大学）；Harvard Medical School（哈佛医学院）
Deep learning-based reconstruction of highly accelerated 3D MRI
GE Research（通用电气研究院）；GE Healthcare（通用电气医疗）；Mayo Clinic（妙佑医疗国际）；Walter Reed National Military Medical Center（沃尔特里德国家军事医疗中心）；Uniformed Services University（美国三军卫生科学大学）
Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples
University of Würzburg（维尔茨堡大学）；Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所IIS）；University Hospital Würzburg（维尔茨堡大学医院）；Research Centre for Natural Sciences（自然科学研究中心）
Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）；United Imaging Healthcare Company Ltd.（联影医疗科技股份有限公司）
ACCELERATION: Sequentially-scanning DECT Imaging Using High Temporal Resolution Image Reconstruction And Temporal Extrapolation
Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；University of Chinese Academy of Sciences（中国科学院大学）
Fast Trajectory-Independent Model-Based Reconstruction Algorithm for Multi-Dimensional Magnetic Particle Imaging
Algorithms for Computer Vision, Imaging and Data Analysis Lab at Darmstadt University of Applied Sciences（达姆斯塔特应用科学大学计算机视觉与成像分析实验室）；Data Science Institute, European University of Technology（欧洲技术大学数据科学研究所）
Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
Department of Electrical \& Computer Engineering, Rice University, Houston TX, USA Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston TX, USA
Compressive lensless endoscopy with partial speckle scanning
UCLouvain（鲁汶大学）；Aix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel（艾克斯-马赛大学、法国国家科学研究中心、中央马赛学校、菲涅耳研究所）；Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC Institute, UCLouvain（分子影像、放疗与肿瘤学中心（MIRO），IREC研究所，鲁汶大学）；Lightcore Tehnologies（Lightcore Technologies公司）
