An important imaging technique in biomedicine, the conventional optical microscopy relies on relatively complicated and bulky lens and alignment mechanics. Based on the Gabor holography, the lensless digital holographic microscopy has the advantages of light weight and low cost. It has developed rapidly and received attention in many fields. However, the finite pixel size at the sensor plane limits the spatial resolution. In this study, we first review the principle of lensless digital holography, then go over some methods to improve image contrast and discuss the methods to enhance the image resolution of the lensless holographic image. Moreover, the applications of lensless digital holographic microscopy in biomedicine are reviewed. Finally, we look forward to the future development and prospect of lensless digital holographic technology.

Limited-projection fluorescence molecular tomography (FMT) has short data acquisition time that allows fast resolving of the three-dimensional visualization of fluorophore within small animal in vivo. However, limited-projection FMT reconstruction suffers from severe ill-posedness because only limited projections are used for reconstruction. To alleviate the ill-posedness, a feasible region extraction strategy based on a double mesh is presented for limited-projection FMT. First, an initial result is rapidly recovered using a coarse discretization mesh. Then, the reconstructed fluorophore area in the initial result is selected as a feasible region to guide the reconstruction using a fine discretization mesh. Simulation experiments on a digital mouse and small animal experiment in vivo are performed to validate the proposed strategy. It demonstrates that the presented strategy provides a good distribution of fluorophore with limited projections of fluorescence measurements. Hence, it is suitable for reconstruction of limited-projection FMT.

Compared with static optical imaging, dynamic optical imaging technology can obtain quantitative pharmacokinetics information, such as the probe metabolism curve, removal rate, and binding potential of the receptor. Accurate segmentation of the region of interest (ROI) is an important step in dynamic optical imaging. Generally, the ROI is manually labeled by researchers based on experience. This will lead to two unavoidable problems. First, manual segmentation of the ROI is very time consuming, especially when there are many sequential dynamic optical images. Second, manual segmentation cannot ensure accuracy when the optical signal gradually decays to a point at which it is difficult to distinguish by using the naked eyes. These problems will inevitably lead to inaccuracy of quantitative results of dynamic optical imaging. Here, we presented a machine learning-based automatic segmentation method to avoid these time-consuming and inaccuracy problems caused by manual segmentation. The K-means clustering algorithm and fuzzy c-means clustering algorithm were implemented to separate the ROI from the background of sequential dynamic optical images. Automatic selection of clustering results was completed by mathematical methods. The accuracy and feasibility of machine learning-based methods were verified by comparing their results with the manual segmentation results. The preliminary results demonstrated that the machine learning-based automatic segmentation has coherent performance with the manual one.

Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction.

This correspondence investigates the multistatic target localization problem, accounting for time offsets of transmitters and sensor position uncertainties. A maximum likelihood estimation (MLE) problem involving the target position, sensor positions and time offsets is formulated by directly using the original measurements. To solve the non-convex MLE problem, an efficient two-step semidefinite relaxation (SDR) method is proposed. Specifically, the SDR technique is employed to solve the MLE problem and then the solution is further refined by estimating the error term. Furthermore, it is analytically demonstrated that the proposed method can achieve the Cram´er-Rao lower bound (CRLB) under low-noise conditions. Simulation results validate the theoretical development and show that the performance of the proposed method outperforms existing algorithms