The annual incidence rate of tumours in the brain and central nervous system (CNS) was 19.89 per 100,000 persons between 2004 and 2008 in the United States. Surgery is a common treatment option for brain and CNS tumours. Typically, biopsy followed by histological analysis is used to confirm tumour types and margin during neurosurgery as an intraoperative diagnostic tool. However, this biopsy method is invasive, sampling number limited and not in real-time. To overcome these problems, many minimally invasive optical techniques, called optical biopsies, have been developed towards intraoperative diagnosis.
The research work carried out in this dissertation focuses on combining the time-resolved fluorescence (TRF) and diffuse reflectance (DR) spectroscopy towards intraoperative tumour margin detection in neurosurgery. Combining these two modalities allows us to obtain additional contrast features, thus potentially improving the diagnostic accuracy. To achieve this goal, first, a clinically compatible integrated TRF-DR spectroscopy instrument was developed for in vivo brain tumour study. An acousto-optical-tunable-filter-based spectrometer was designed to acquire the time-resolved fluorescence signal. A dual-modality fibre optic probe was used to collect the TRF and DR signals in a small volume. The system’s capabilities of resolving fluorescence spectrum and lifetime, and optical properties were characterized and validated using tissue phantoms. Second, in order to retrieve the fluorescence impulse response function accurately from measured fluorescence signals, a robust Laguerre-based deconvolution method was optimized by using the constrained linear least squares fitting and high order Laguerre function basis. This optimized Laguerre-based deconvolution method overcomes the over-fitting problem introduced by low signal-to-noise ratio and complex fitting model. Third, an ex vivo clinical study of brain tumours was carried out using the TRF and DR spectroscopy. Fluorescence spectra and lifetime features were selected to classify various tumour types. The sensitivity and specificity of meningioma grade I differentiated from meningioma grade II are both 100%. Finally, in order to increase the measurement tissue volume and obtain imaging contrast features, a scanning-based hyperspectral fluorescence lifetime imaging system was developed. This setup can provide time-, space-, spectrum- resolved multi-dimensional images for tumour margin detection.