Sub-Diffraction and Sub-Pixel Microscopic Deconvolution

亚衍射和亚像素显微反卷积

• 批准号: 7285666
• 负责人: TIMOTHY J HOLMES
• 金额: $ 28.17万
• 依托单位: LICKENBROCK TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7285666
• 关键词: Acceleration; Algorithms; Biological; Biological Sciences; Bite; Cells; Compatible; Complex; Computer Simulation; Computer Systems; Computer software; Data; Data Set; Development; Dimensions; Electron Microscope; Electron Microscopy; Financial compensation; Fluorescence; Gold; Image; Imagery; Lasers; Libraries; Life; Light; Light Microscope; Lighting; Methods; Microscope; Microscopic; Modality; Modeling; Modification; Morphologic artifacts; Noise; Operating System; Optics; Performance; Phase; Photons; Process; Pupil; Reporting; Research; Research Personnel; Research Project Grants; Resolution; Retrieval; Sampling; Scanning; Specimen; Speed; Standards of Weights and Measures; Structure; Techniques; Technology; Testing; Time; base; blind; cost; design; image processing; improved; instrumentation; lens; light microscopy; prevent; reconstruction; research study; restoration; size

DESCRIPTION (provided by applicant): The objective of this project is to increase the effective resolution of optical light microscopes using deconvolution algorithms specifically designed for restoring imagery at a sub-pixel level. Light microscopy (LM) of living cells is typically diffraction limited to a maximum resolution of approximately 200nm, and resolution improvements are often considered to require hardware modifications such as structured illumination. The sub-pixel deconvolution software is intended to give life-science researchers an opportunity to increase the resolvability of fine structures within their 3D specimens, using their existing instrumentation and at a low cost. The sub-pixel algorithm is based on maximum-likelihood deconvolution and analytic continuation of photon-limited data. Particular importance is placed on developing algorithm acceleration techniques to reduce processing requirements and make the software commercially attractive. Methods will be investigated that model the effect of the camera pixel dimension and noise. Noise suppression methods are important to improve algorithm robustness in low-light imaging situations, and to retain fine features while preventing unwanted artifacts. The deconvolution will employ phase retrieval methods to estimate the wavefront error at the exit pupil of the microscope directly from the observed data. This approach to blind deconvolution will improve the ability of the algorithm to adapt the point spread function to subtle aberrations and tolerances in the objective lens specifications. Additionally, the sub-pixel algorithm will enable under-sampled imagery using on-chip camera binning or large pixels, and optical sections that are spaced beyond the Nyquist limit, to be correctly processed. The algorithms will be extended to be compatible with widefield fluorescence, transmitted light brightfield, spinning disk confocal and laser scanning ponfocal modalities. Performance will be verified using manufactured test targets and biological specimens with known structures.

描述（由申请人提供）：该项目的目标是使用专门为恢复亚像素级图像而设计的反卷积算法来提高光学显微镜的有效分辨率。活细胞的光学显微镜 (LM) 通常衍射限制在大约 200 nm 的最大分辨率，并且分辨率的提高通常被认为需要硬件修改，例如结构照明。亚像素反卷积软件旨在为生命科学研究人员提供一个机会，利用现有仪器以低成本提高 3D 样本中精细结构的分辨率。子像素算法基于最大似然反卷积和光子有限数据的分析延拓。特别重要的是开发算法加速技术，以降低处理要求并使软件具有商业吸引力。将研究对相机像素尺寸和噪声的影响进行建模的方法。噪声抑制方法对于提高低光成像情况下算法的稳健性以及保留精细特征并防止不必要的伪影非常重要。反卷积将采用相位检索方法直接从观察到的数据估计显微镜出瞳处的波前误差。这种盲反卷积方法将提高算法使点扩散函数适应物镜规格中的细微像差和公差的能力。此外，子像素算法将能够正确处理使用片上相机合并或大像素以及间隔超出奈奎斯特极限的光学部分的欠采样图像。该算法将扩展为与宽场荧光、透射光明场、旋转盘共焦和激光扫描庞焦模式兼容。将使用制造的测试目标和具有已知结构的生物样本来验证性能。