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Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey [0.03%] 基于物理/模型和数据驱动的低剂量CT方法概述
Wenjun Xia,Hongming Shan,Ge Wang et al. Wenjun Xia et al.
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction ne...
High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions [0.03%] 基于物理建模和数据驱动机器学习的高维磁共振时空谱成像：当前进展与未来方向
Fan Lam,Xi Peng,Zhi-Pei Liang Fan Lam
Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical...
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging [0.03%] 基于物理的深度学习在计算磁共振成像中的应用：结合物理和机器学习以改善医学影像学
Kerstin Hammernik,Thomas Küstner,Burhaneddin Yaman et al. Kerstin Hammernik et al.
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments...
Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging [0.03%] 基于机器学习的预测神经影像解释中的挑战和解决方案：解读大脑生物标志物
Rongtao Jiang,Choong-Wan Woo,Shile Qi et al. Rongtao Jiang et al.
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding ...
Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness [0.03%] 矩阵和张量分解中的可重复性：关注模型匹配、解释性和唯一性
Tülay Adali,Furkan Kantar,M A B Siddique Akhonda et al. Tülay Adali et al.
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective [0.03%] 基于信号处理的生物医学图像重建与增强的无监督深度学习方法综述
Mehmet Akçakaya,Burhaneddin Yaman,Hyungjin Chung et al. Mehmet Akçakaya et al.
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the diffic...
Light-Field Microscopy for Optical Imaging of Neuronal Activity: When Model-Based Methods Meet Data-Driven Approaches [0.03%] 基于模型方法与数据驱动策略在神经元活动光学成像中的结合——光场显微镜技术
Pingfan Song,Herman Verinaz Jadan,Carmel L Howe et al. Pingfan Song et al.
Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieve this goal is to be able to observe the dynamics of large populations of neurons over a large area of ...
Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning [0.03%] 结构化低秩算法：理论、磁共振应用及其与机器学习的联系
Mathews Jacob,Merry P Mani,Jong Chul Ye Mathews Jacob
In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation. Th...
Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery [0.03%] 用于磁共振成像的现成方法：使用去噪算法进行图像恢复
Rizwan Ahmad,Charles A Bouman,Gregery T Buzzard et al. Rizwan Ahmad et al.
Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data...
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues [0.03%] 并行磁共振成像重建的深度学习方法：当前研究趋势及存在问题综述
Florian Knoll,Kerstin Hammernik,Chi Zhang et al. Florian Knoll et al.
Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep ...