Oberlaender M, Broser P J, Sakmann B, Hippler S
Max Planck Institute of Neurobiology Research Group 'Cortical Column In Silico', Am Klopferspitz 18, Martinsried 82152, Germany.
J Microsc. 2009 Feb;233(2):275-89. doi: 10.1111/j.1365-2818.2009.03118.x.
We present a novel approach for deconvolution of 3D image stacks of cortical tissue taken by mosaic/optical-sectioning technology, using a transmitted light brightfield microscope. Mosaic/optical-sectioning offers the possibility of imaging large volumes (e.g. from cortical sections) on a millimetre scale at sub-micrometre resolution. However, a blurred contribution from out-of-focus light results in an image quality that usually prohibits 3D quantitative analysis. Such quantitative analysis is only possible after deblurring by deconvolution. The resulting image quality is strongly dependent on how accurate the point spread function used for deconvolution resembles the properties of the imaging system. Since direct measurement of the true point spread function is laborious and modelled point spread functions usually deviate from measured ones, we present a method of optimizing the microscope until it meets almost ideal imaging conditions. These conditions are validated by measuring the aberration function of the microscope and tissue using a Shack-Hartmann sensor. The analysis shows that cortical tissue from rat brains embedded in Mowiol and imaged by an oil-immersion objective can be regarded as having a homogeneous index of refraction. In addition, the amount of spherical aberration that is caused by the optics or the specimen is relatively low. Consequently the image formation is simplified to refraction between the embedding and immersion medium and to 3D diffraction at the finite entrance pupil of the objective. The resulting model point spread function is applied to the image stacks by linear or iterative deconvolution algorithms. For the presented dataset of large 3D images the linear approach proves to be superior. The linear deconvolution yields a significant improvement in signal-to-noise ratio and resolution. This novel approach allows a quantitative analysis of the cortical image stacks such as the reconstruction of biocytin-stained neuronal dendrites and axons.
我们提出了一种新颖的方法，用于对通过镶嵌/光学切片技术，利用透射光明场显微镜拍摄的皮质组织三维图像堆栈进行去卷积处理。镶嵌/光学切片技术能够以亚微米分辨率对毫米尺度的大体积样本（如皮质切片）进行成像。然而，离焦光产生的模糊效应会导致图像质量下降，通常无法进行三维定量分析。只有通过去卷积进行去模糊处理后，才有可能进行这种定量分析。最终的图像质量在很大程度上取决于用于去卷积的点扩散函数与成像系统特性的相似程度。由于直接测量真实的点扩散函数较为繁琐，且建模的点扩散函数通常与测量值存在偏差，因此我们提出了一种优化显微镜的方法，使其达到近乎理想的成像条件。通过使用夏克-哈特曼传感器测量显微镜和组织的像差函数来验证这些条件。分析表明，嵌入Mowiol并使用油浸物镜成像的大鼠脑皮质组织可被视为具有均匀的折射率。此外，由光学系统或样本引起的球差量相对较低。因此，图像形成过程可简化为嵌入介质与浸液介质之间的折射以及物镜有限孔径处的三维衍射。通过线性或迭代去卷积算法将所得的模型点扩散函数应用于图像堆栈。对于所呈现的大型三维图像数据集，线性方法被证明更为优越。线性去卷积在信噪比和分辨率方面有显著提高。这种新颖的方法使得对皮质图像堆栈进行定量分析成为可能，例如重建生物胞素染色的神经元树突和轴突。
