Laboratory of High-Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA.
State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
Nat Biotechnol. 2020 Nov;38(11):1337-1346. doi: 10.1038/s41587-020-0560-x. Epub 2020 Jun 29.
The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an 'unmatched back projector' accelerates deconvolution relative to the classic Richardson-Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes.
光学显微镜获得的图像对比度和分辨率可以通过对同一样本的多个视图进行反卷积和计算融合来提高，但对于大型数据集，这些方法的计算成本很高。在这里，我们描述了算法和软件设计方面的理论和实际进展，使得图像处理时间比以前的方法快了十倍到几千倍。首先，我们表明，与经典的 Richardson-Lucy 算法相比，“不匹配的反向投影器”至少加速了十倍的反卷积。其次，基于图形处理单元的三维图像配准将处理速度相对于 CPU 处理提高了 10 到 100 倍。第三，深度学习可以提供进一步的加速，特别是对于具有空间变化点扩散函数的反卷积。我们从亚细胞到毫米空间尺度的各种样本上展示了我们的方法，包括单细胞、胚胎和透明组织。最后，我们展示了在具有更高空间分辨率的新型显微镜上的性能增强，包括双视图透明组织光片显微镜和反射晶格光片显微镜。
