Deep Learning for Imaging Non-Static Objects

用于非静态物体成像的深度学习

• 批准号: 517586365
• 负责人: Professor Dr. Reinhard Heckel
• 金额: --
• 依托单位: Professur für Machine Learning
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/517586365?language=en
• 关键词: Deep; Learning; Imaging; Non; Static

Deep neural networks have emerged as highly successful tools for image reconstruction problems. For example, neural networks significantly accelerate magnetic resonance imaging (MRI), an important medical imaging technique. This success is largely attributed to neural networks being able to learn excellent priors (models) for static objects from data. However, objects in many important scientific and medical imaging systems change significantly during measurement acquisition. For example, while acquiring cardiac MRI data, the heart goes through hundreds of cardiac cycles. Second, a protein imaged with cryogenic electron microscopy (cryo-EM) is in a continuum of configurations during acquisition. Current methods for imaging such non-static objects cast reconstruction as a series of static reconstruction problems. This induces a systematic error and significantly limits image quality. The goal of this project is to develop new deep learning techniques for imaging non-static objects. By modeling non-static objects with neural networks, we will address two outstanding problems in imaging: enabling free-breathing MRI and inferring high-resolution continuous 3D-configurations of proteins with cryo-EM. First, we will develop methods that cast imaging of a non-static object as fitting a neural network to measurement data. This approach will, for the first time, enable high-resolution free-breathing cardiac MRI, and more broadly alleviate motion artifacts in MRI. Second, we will develop networks trained end-to-end to reconstruct non-static objects, which will enable fast high-resolution cine cardiac MRI. Third, we will develop methods imposing strong learned neural network priors on proteins for enabling inferring the continuous 3D-configurations of proteins with cryo-EM, considered the holy grail of structural biology. Finally, we will develop mathematical reconstruction and robustness guarantees for imaging non-static objects.

深度神经网络已经成为解决图像重建问题的非常成功的工具。例如，神经网络显著加速了磁共振成像(MRI)，这是一种重要的医学成像技术。这一成功在很大程度上归功于神经网络能够从数据中学习静态对象的优秀先验(模型)。然而，在许多重要的科学和医学成像系统中，物体在测量采集过程中会发生显著的变化。例如，在获取心脏MRI数据的同时，心脏经历了数百个心脏周期。其次，用低温电子显微镜(Cryo-EM)成像的蛋白质在采集过程中处于一系列构型中。现有的对这种非静态物体成像的方法将重建归结为一系列静态重建问题。这会导致系统误差，并显著限制图像质量。该项目的目标是开发用于成像非静态对象的新的深度学习技术。通过使用神经网络对非静态对象进行建模，我们将解决成像中的两个突出问题：实现自由呼吸的MRI和使用冷冻-EM推断蛋白质的高分辨率连续3D构型。首先，我们将开发将非静态对象的成像转换为对测量数据进行神经网络拟合的方法。这种方法将首次实现高分辨率自由呼吸心脏MRI，并更广泛地减轻MRI中的运动伪影。其次，我们将开发端到端训练的网络来重建非静态对象，这将使快速高分辨率电影心脏MRI成为可能。第三，我们将开发将强大的学习神经网络先验施加到蛋白质上的方法，以便能够使用被认为是结构生物学的圣杯的冷冻EM来推断蛋白质的连续3D构型。最后，我们将开发数学重建和稳健性保证成像的非静态对象。