Joint Iterative Reconstruction and Motion Compensation for Optical Coherence Tomography Angiography

光学相干断层扫描血管造影的联合迭代重建和运动补偿

• 批准号: 414781207
• 负责人: Professor Dr.-Ing. Andreas Maier
• 金额: --
• 依托单位: Lehrstuhl für Informatik 5: Mustererkennung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/414781207?language=en
• 关键词: Joint; Iterative; Reconstruction; Motion; Compensation; Joint; Iterative; Reconstruction; Motion; Compensation

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.

尽管八颗成像显示了改善眼科患者护理的巨大希望，但其全部潜力受到限制。迫切需要强大而准确的运动检测和校正以及改进的八八个处理算法，以降低噪声水平并提高图像质量和分辨率。此外，只有很少的开源软件可以使来自世界各地的研究人员能够产生高质量的图像。 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 OCTA信号提取中的基于压缩感应的正则化方法，并从目标A中整合了完整的3-D运动模型，包括插值过程，结合了正确的物理噪声模型。C）精确学习重建，从目标B中增强了从物理B中正确正确的模型与其他深度学习技术，以使数据 - 稀疏的稀疏域和最佳的Navigator模式供应，以便在该项目中创建了一个项目。