a GRIN lens [4], an acousto-optic tunable lens [5], or an axicon lens [6]. These methods are effective, but are accompanied by increased system cost and decreased imaging speed. On the other hand, the digital focusing method improves the lateral resolution by calculating the field distribution of the image obtained at an out-of-focus region through digital computation without any modification of the system's hardware configuration.  
The digital focusing method has been applied for improvement of both axial and lateral resolution by means of deconvolution. The axial deconvolution method has been demonstrated to enhance the depth measurement range by applying dispersion compensation [7]. Likewise, the methods based on spectral shaping have also been demonstrated to enhance the depth resolution of OCT [8]. Similarly, lateral resolution improvements have been demonstrated with the deconvolution methods [9; 10], inverse scattering algorithm [11], Gabor-based fusion technique [12], guide-star-based technique [13], scalar diffraction algorithm [14; 15], and hybrid hardware and software method [16]. However, most of the aforementioned methods require some prior knowledge of the sample and the imaging system, such as a refractive index of samples, the point spread function (PSF) of the system, and the focal position of the probe beam. To address this, a digital focusing of OCT images without prior knowledge has been demonstrated by automatically finding the distance between the de-focal plane and the focal plane via scalar diffraction theory and information entropy [17]. However, application of this technique to in vivo imaging applications--where the challenges are obvious due to motion artifact--have not yet been demonstrated.  
Recently, OCT has been considered as a promising tool for brain imaging, providing 3D structural and vascular images in vivo [18; 19]. Although the digital focusing method may prove useful for enhancing the depth measurement range in brain OCT imaging, applying the digital focusing method to in vivo brain imaging applications is challenging. Digital refocusing of blurred images may be critical for in vivo brain imaging because the small vessels get easily blurred due to their small size or low contrast. Such digital refocusing has not been demonstrated for in vivo brain imaging so far. To solve this problem, we have utilized the vascular structure of the brain as an image singularity or as a guiding star for the digital refocusing method. However, in the OCT brain images, it is difficult to identify the image singularity. The blood vessel is unclear in an OCT image. Therefore, we used the OCTA (OCT angiography) modality to capture the blood flow that cannot be seen in the OCT intensity images. Since blood vessels are good image singularities in OCTA [20; 21; 22], they can act as good guiding stars for automatically refocusing the OCT image.  
In this paper, we propose and present a specific method that can be applied for automatic digital refocusing of in vivo brain OCT images. First, we describe the entire methodology for automatically recovering defocused images. After that, the performance evaluation of proposed method by imaging the brain of a mouse in vivo is described. Finally, the brain image refocused automatically by the proposed method is compared to the original image.