Elucidating the normal structure and distribution of cerebral vascular system is fundamental for understanding its function. However, studies on visualization and whole-brain quantification of vasculature with cellular resolution are limited. Here, we explored the structure of vasculature at the whole-brain level using the newly developed CLARITY technique. Adult male C57BL/6J mice undergoing transient middle cerebral artery occlusion and Tie2-RFP transgenic mice were used. Whole mouse brains were extracted for CLARITY processing. Immunostaining was performed to label vessels. Customized MATLAB code was used for image processing and quantification. Three-dimensional images were visualized using the Vaa3D software. Our results showed that whole mouse brain became transparent using the CLARITY method. Three-dimensional imaging and visualization of vasculature were achieved at the whole-brain level with a 1-μm voxel resolution. The quantitative results showed that the fractional vascular volume was 0.018 ± 0.004 mm3 per mm3, the normalized vascular length was 0.44 ± 0.04 m per mm3, and the mean diameter of the microvessels was 4.25 ± 0.08 μm. Furthermore, a decrease in the fractional vascular volume and a decrease in the normalized vascular length were found in the penumbra of ischemic mice compared to controls (p < 0.05). In conclusion, CLARITY provides a novel approach for mapping vasculature in the whole mouse brain at cellular resolution. CLARITY-optimized algorithms facilitate the assessment of structural change in vasculature after brain injury.

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In image and video processing field, an effective compression algorithm should remove not only the statistical redundancy information but also the perceptually insignificant component from the pictures. Just-noticeable distortion (JND) profile is an efficient model to represent those perceptual redundancies. Human eyes are usually not sensitive to the distortion below the JND threshold. In this paper, a DCT based JND model for monochrome pictures is proposed. This model incorporates the spatial contrast sensitivity function (CSF), the luminance adaptation effect, and the contrast masking effect based on block classification. Gamma correction is also considered to compensate the original luminance adaptation effect which gives more accurate results. In order to extend the proposed JND profile to video images, the temporal modulation factor is included by incorporating the temporal CSF and the eye movement compensation. Moreover, a psychophysical experiment was designed to parameterize the proposed model. Experimental results show that the proposed model is consistent with the human visual system (HVS). Compared with the other JND profiles, the proposed model can tolerate more distortion and has much better perceptual quality. This model can be easily applied in many related areas, such as compression, watermarking, error protection, perceptual distortion metric, and so on.