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Sentinel lymph node (SLN) in vivo detection is vital in breast cancer surgery. A new near-infrared fluorescence-based surgical navigation system (SNS) imaging software, which has been developed by our research group, is presented for SLN detection surgery in this paper. The software is based on the fluorescence-based surgical navigation hardware system (SNHS) which has been developed in our lab, and is designed specifically for intraoperative imaging and postoperative data analysis. The surgical navigation imaging software consists of the following software modules, which mainly include the control module, the image grabbing module, the real-time display module, the data saving module and the image processing module. And some algorithms have been designed to achieve the performance of the software, for example, the image registration algorithm based on correlation matching. Some of the key features of the software include: setting the control parameters of the SNS; acquiring, display and storing the intraoperative imaging data in real-time automatically; analysis and processing of the saved image data. The developed software has been used to successfully detect the SLNs in 21 cases of breast cancer patients. In the near future, we plan to improve the software performance and it will be extensively used for clinical purpose.

In mathematics, optical molecular imaging including bioluminescence tomography (BLT), fluorescence tomography (FMT) and Cerenkov luminescence tomography (CLT) are concerned with a similar inverse source problem. They all involve the reconstruction of the 3D location of a single/multiple internal luminescent/fluorescent sources based on 3D surface flux distribution. To achieve that, an accurate fusion between 2D luminescent/fluorescent images and 3D structural images that may be acquired form micro-CT, MRI or beam scanning is extremely critical. However, the absence of a universal method that can effectively convert 2D optical information into 3D makes the accurate fusion challengeable. In this study, to improve the fusion accuracy, a new fusion method for dual-modality tomography (luminescence/fluorescence and micro-CT) based on natural light surface reconstruction (NLSR) and iterated closest point (ICP) was presented. It consisted of Octree structure, exact visual hull from marching cubes and ICP. Different from conventional limited projection methods, it is 360° free-space registration, and utilizes more luminescence/fluorescence distribution information from unlimited multi-orientation 2D optical images. A mouse mimicking phantom (one XPM-2 Phantom Light Source, XENOGEN Corporation) and an in-vivo BALB/C mouse with implanted one luminescent light source were used to evaluate the performance of the new fusion method. Compared with conventional fusion methods, the average error of preset markers was improved by 0.3 and 0.2 pixels from the new method, respectively. After running the same 3D internal light source reconstruction algorithm of the BALB/C mouse, the distance error between the actual and reconstructed internal source was decreased by 0.19 mm.

Photoacoustic imaging is a noninvasive biomedical imaging modality for visualizing the internal structure and function of soft tissues. In this paper we present a combined iterative reconstruction algorithm to recover the distribution of optical absorption coefficients. Such a combination of the simultaneous algebraic reconstruction technique (SART) algorithm and the filtered back projection (FBP) algorithm bears the promise to improve the image quality of fast but not accurate. The different between the present proposal and traditional ART algorithm is that we computer a full error image with FBP, which is used to projection differences between original and reconstruction image, and to update the solution in each iteration step. The combined iterative reconstruction algorithm works well in rectification of the measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption coefficients. Numerical simulations demonstrate that the proposed algorithm effectively reduces the artifacts and blurs, and yields better quality of reconstruction image than that with FBP algorithm in terms of both accuracy and robustness to noise.

Solution with adjustable sparsity to tomographic imaging of Cerenkov photons is presented in this work. The sparsity of radionuclides' distribution in tissues is an objective but unknown fact, and the inverse model of qualitative data is an ill-posed problem. Based on the optimization technique, the uniqueness of numerical solution to the ill-conditioned compact operator can be guaranteed by use of sparse regularization with the approximate message-passing (AMP) method. After absorbing formulations with the AMP, we analyzed the behavior of the hard thresholding operator. Iteratively numerical solutions were used to approximate the real light source by assuming the number of non-zero solution in manual mode. This modified AMP algorithm was performed in numerical simulation and physical experiments with 2-[18F]fluoro-2-deoxy-D-glucose. Experimental results indicated that the proposed method was a kind of low-complexity iterative thresholding algorithms for reconstructing 3D sparse distribution from a small set of optical measurements.