Estimation of Fiber Orientations Using Neighborhood Information
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Maryland School of Medicine（马里兰大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chuyang Ye, Jiachen Zhuo, Rao P. Gullapalli, Jerry L. Prince
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Journal paper accepted in Medical Image Analysis. 35 pages and 16 figures
Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of fiber orientations (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted l1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.
Fiber Orientation Estimation Guided by a Deep Network
Johns Hopkins University（约翰霍普金斯大学）；National Laboratory of Pattern Recognition & Brainnetome Center, Institute of Automation, Chinese Academy of Sciences（模式识别国家重点实验室与脑网络组研究中心，中国科学院自动化研究所）
Local Fiber Orientation from X-ray Region-of-Interest Computed Tomography of large Fiber Reinforced Composite Components
Ford Research and Innovation Center Aachen, Ford-Werke GmbH（福特汽车公司亚琛福特研究与创新中心）；Hochschule Darmstadt（达姆施塔特应用技术大学）；Fraunhofer-Institut für Techno-und Wirtschaftsmathematik（弗劳恩霍夫工业数学研究所）；Fraunhofer-Entwicklungszentrum Röntgentechnik（弗劳恩霍夫X射线技术开发中心）；Montaplast GmbH（蒙塔普拉斯有限公司）
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
TU/e（埃因霍温理工大学）；INRIA Sophia Antipolis（法国国家信息与自动化研究所索菲亚-安提波利斯研究中心）；Academic Center for Epileptology Kempenhaeghe（肯彭哈赫癫痫学术中心）；Maastricht UMC+（马斯特里赫特大学医学中心+）；Université de Sherbrooke（舍布鲁克大学）
Semi-supervised Large-scale Fiber Detection in Material Images with Synthetic Data
University of South Carolina（南卡罗来纳大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Cleveland State University（克利夫兰州立大学）；Air Force Research Lab（空军研究实验室）
Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains
Signal Processing Laboratory (LTS5)（信号处理实验室）；École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院洛桑分校）；Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)（洛桑大学医院放射科及洛桑大学）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息学院）；CIBM Center for Biomedical Imaging（生物医学成像中心）；Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital and Harvard Medical School（波士顿儿童医院放射科计算放射实验室及哈佛医学院）；Department of Radiological Sciences, University of California Irvine（加州大学 Irvine 分校放射科学系）
Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers
AImotion Bavaria, Technische Hochschule Ingolstadt（AImotion Bavaria，英戈尔施塔特应用技术大学）；TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；TUM School of Natural Sciences, Technical University of Munich（慕尼黑工业大学自然科学学院）
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev（内盖夫本-古里安大学兹洛托夫斯基神经科学中心）；Dalhousie University（达尔豪斯大学）
