RESUMO
When using traditional Euler deconvolution optimization strategies, it is difficult to distinguish between anomalies and their corresponding Euler tails (those solutions are often distributed outside the anomaly source, forming "tail"-shaped spurious solutions, i.e., misplaced Euler solutions, which must be removed or marked) with only the structural index. The nonparametric estimation method based on the normalized B-spline probability density (BSS) is used to separate the Euler solution clusters and mark different anomaly sources according to the similarity and density characteristics of the Euler solutions. For display purposes, the BSS needs to map the samples onto the estimation grid at the points where density will be estimated in order to obtain the probability density distribution. However, if the size of the samples or the estimation grid is too large, this process can lead to high levels of memory consumption and excessive computation times. To address this issue, a fast linear binning approximation algorithm is introduced in the BSS to speed up the computation process and save time. Subsequently, the sample data are quickly projected onto the estimation grid to facilitate the discrete convolution between the grid and the density function using a fast Fourier transform. A method involving multivariate B-spline probability density estimation based on the FFT (BSSFFT), in conjunction with fast linear binning appropriation, is proposed in this paper. The results of two random normal distributions show the correctness of the BSS and BSSFFT algorithms, which is verified via a comparison with the true probability density function (pdf) and Gaussian kernel smoothing estimation algorithms. Then, the Euler solutions of the two synthetic models are analyzed using the BSS and BSSFFT algorithms. The results are consistent with their theoretical values, which verify their correctness regarding Euler solutions. Finally, the BSSFFT is applied to Bishop 5X data, and the numerical results show that the comprehensive analysis of the 3D probability density distributions using the BSSFFT algorithm, derived from the Euler solution subset of x0,y0,z0, can effectively separate and locate adjacent anomaly sources, demonstrating strong adaptability.
RESUMO
In this paper, a white-light full-field optical coherence tomography is developed to provide three-dimensional imaging of the development of a mouse embryo with ultrahigh-resolution. Spatial resolution of 1.8 µm×1.12 µm (transverse×axial) is achieved owing to the extremely short coherence length of the light source and optimized compensation of dispersion mismatch. A shot-noise limited detection sensitivity of 80 dB is obtained at an acquisition time of 5 seconds per image. To enable in vivo imaging of the mouse embryo development, a homemade incubator is applied to provide appropriate CO2 concentration, temperature, and humidity. An electronic light shutter is used to control the light source in order to avoid unnecessary exposure to the embryo development when the sample is not being scanned. To demonstrate our method, in vivo time series two-dimensional images of the in vitro fertilization process of mouse oocytes at the germinal vesicles stage are presented.
