Joint Iterative Reconstruction and Motion Compensation for Optical Coherence Tomography Angiography

虽然OCTA成像显示出改善眼科患者护理的巨大前景，但其全部潜力有限。迫切需要鲁棒且准确的运动检测和校正以及将降低噪声水平并提高图像质量和分辨率的改进的OCTA处理算法。此外，只有很少的开源软件可以让世界各地的研究人员制作高质量的图像。为了满足这些需要，该项目将努力实现以下目标：A）改进的运动补偿，包括使用OCTA信号以提高数据一致性，允许仿射运动（例如旋转和缩放）的更准确的运动模型，以及对源自基于相机的眼睛跟踪或基于导航器的运动估计方法的替代信号的整合，从而产生对所有采集的A扫描数据的全3D校正。B）物理上正确的OCTA信号提取，其在OCTA信号提取中采用基于压缩感测的正则化方法，并整合全3D校正。C）精确学习重建，其利用附加的深度学习技术来学习数据，从而增强来自目标B的物理上正确的模型。最佳稀疏域和最佳导航模式的运动信号提取。在这个项目中创建的所有软件将作为开源软件发布。

While OCTA imaging has shown great promise for improving patient care in ophthalmology, its full potential has been limited. There is an urgent need for robust and accurate motion detection and correction and improved OCTA processing algorithms that will decrease noise levels and improve image quality and resolution. Furthermore, there is only few open-source software that enables researchers from around the world to produce high quality images. In order to address these needs, the project will pursue the following objectives:A) Improved motion compensation including the use of the OCTA signal for improved data consistency, more accurate motion models that allow affine motions such as rotation and scaling, and integration of surrogate signals stemming from camera-based eye-tracking or navigator-based motion estimation approaches yielding a full 3-D correction for all acquired A-scan data.B) Physically correct OCTA signal extraction that employs compressive sensing-based regularization approaches in the OCTA signal extraction and integrates the full 3-D motion model from Objective A including the interpolation process in combination with correct physical noise models.C) Precision Learning Reconstruction that augments the physically correct model from Objective B with additional deep learning techniques to learn data-optimal sparse domains and optimal navigator patterns for motion signal extraction.All software created in this project will be published as open source software.