Due to its ability of optical sectioning and low phototoxicity, z-stacking light-sheet microscopy has been the tool of choice for in vivo imaging of the zebrafish brain. To image the zebrafish brain with a large field of view, the thickness of the Gaussian beam inevitably becomes several times greater than the system depth of field (DOF), where the fluorescence distributions outside the DOF will also be collected, blurring the image. In this paper, we propose a 3D deblurring method, aiming to redistribute the measured intensity of each pixel in a light-sheet image to in situ voxels by 3D deconvolution. By introducing a Hessian regularization term to maintain the continuity of the neuron distribution and using a modified stripe-removal algorithm, the reconstructed z-stack images exhibit high contrast and a high signal-to-noise ratio. These performance characteristics can facilitate subsequent processing, such as 3D neuron registration, segmentation, and recognition.

中文翻译：

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用于高对比度和高 SNR 体积成像的厚光片 z 堆栈的 3D Hessian 反卷积

由于其光学切片能力和低光毒性，z 堆叠光片显微镜已成为斑马鱼脑体内成像的首选工具。为了对具有大视野的斑马鱼大脑进行成像，高斯光束的厚度不可避免地变得比系统景深 (DOF) 大几倍，其中也将收集 DOF 之外的荧光分布，从而模糊图像。在本文中，我们提出了一种 3D 去模糊方法，旨在通过 3D 反卷积将光片图像中每个像素的测量强度重新分配到原位体素。通过引入 Hessian 正则化项来保持神经元分布的连续性，并使用改进的条纹去除算法，重建的 z-stack 图像表现出高对比度和高信噪比。