To locate tumor accurately during surgery has always been a challenging problem in the field of clinical medicine. Radionuclide imaging technique, computed tomography technique and magnetic resonance technique are difficult to realize real-time intraoperative imaging. Optical molecular imaging (OMI) is a useful method for intraoperative imaging, which has received increased attention. As an important modality of OMI, fluorescence molecular imaging is able to provide high sensitivity and real-time image-guidance for surgeons. In recent years, fluorescence-based surgical navigation imaging for intraoperative detection of sentinel lymph node and tumor is evaluated as a new method. Several research teams have successfully developed different surgical navigation systems with fluorescent molecular imaging technique. The imaging software is the key part of the surgical navigation system (SNS) with OMI due to its ability of real-time data process, image display and data transfer. In this thesis, researches on imaging software development and clinical applications for SNS with OMI are conducted, and the features of the imaging software are introduced in detail. The main contributions of the thesis are listed as follows: （1） We designed the software architecture based on the clinical requirements. The software architecture consists of the following 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. （2） We implemented the function of the imaging software. The development tools as well as the implementation details of the imaging software were introduced in detail. （3） We conducted the preclinical and clinical study using the SNS on sentinel lymph node (SLN) detection in breast cancer patients. In the preclinical study, we explored the optimized injection time and dosage of indocyanine green (ICG) for SLN detection. In the clinical study, we compared of the ICG and methylene blue dye methods in detection of SLN in breast cancer patients. The results demonstrated that SLN detection with our SNS was practical and applicable.
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