Coherent anti-Stokes Raman scattering (CARS) microscopy can resolve the chemical components and distribution of living biological systems in a label-free manner and is favored in several disciplines. Current CARS microscopes typically use bulky, high-performance solid-state lasers, which are expensive and sensitive to environmental changes. With their relatively low cost and environmental sensitivity, supercontinuum fiber (SF) lasers with a small footprint have found increasing use in biomedical applications. Upon these features, in this paper, we homebuilt a lowcost CARS microscope based on a SF laser module (scCARS microscope). This SF laser module is specially customized by adding a time-synchronized seed source channel to the SF laser to form a dual-channel output laser. The performance of the scCARS microscope is evaluated with dimethyl sulfoxide, whose results confirm a spatial resolution of better than 500 nm and a detection sensitivity of millimolar concentrations. The dual-color imaging capability is further demonstrated by imaging different species of mixed microspheres. We finally explore the potential of our scCARS microscope by mapping lipid droplets in different cancer cells and corneal stromal lenses.

Monte Carlo (MC) method is a statistical method for simulating photon propagation in media in the optical molecular imaging field. However, obtaining an accurate result using the method is quite time-consuming, especially because the boundary of the media is complex. A voxel classification method is proposed to reduce the computation cost. All the voxels generated by dividing the media are classified into three types (outside, boundary, and inside) according to the position of the voxel. The classified information is used to determine the relative position of the photon and the intersection between photon path and media boundary in the MC method. The influencing factors and effectiveness of the proposed method are analyzed and validated by simulation experiments.

We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography (BLT). In the Bayesian approach, five kinds of a priori information are incorporated, reducing the ill-posedness of BLT. Specifically, source sparsity characteristic is considered to promote reconstruction results. Considering the computational burden in the multispectral case, a series of strategies is adopted to improve computational efficiency, such as optimal permissible source region strategy and node model of the finite element method. The performance of the proposed algorithm is validated by a heterogeneous three-dimensional (3D) micron scale computed tomography atlas and a mouse-shaped phantom. Reconstructed results demonstrate the feasibility and effectiveness of the proposed algorithm.