Diffuse correlation spectroscopy (DCS) is a non-invasive optical modality which can be used to measure cerebral blood flow (CBF) in real-time. It has important potential applications in clinical monitoring, as well as in neuroscience and the development of a non-invasive brain-computer interface. However, a trade-off exists between the signal-to-noise ratio (SNR) and imaging depth, and thus CBF sensitivity, of this technique. Additionally, as DCS is a diffuse optical technique, it is limited by a lack of inherent depth discrimination within the illuminated region of each source-detector pair, and the CBF signal is therefore also prone to contamination by the extracerebral tissues which the light traverses.
Placing a particular emphasis on scalability, affordability, and robustness to ambient light, in this work I demonstrate a novel approach which fuses the fields of digital holography and DCS: holographic Fourier domain DCS (FD-DCS). The mathematical formalism of FD-DCS is derived and validated, followed by the construction and validation (for both in vitro and in vivo experiments) of a holographic FD-DCS instrument. By undertaking a systematic SNR performance assessment and developing a novel multispeckle denoising algorithm, I demonstrate the highest SNR gain reported in the DCS literature to date, achieved using scalable and low-cost camera-based detection.
With a view to generating a forward model for holographic FD-DCS, in this thesis I propose a novel framework to simulate statistically accurate time-integrated dynamic speckle patterns in biomedical optics. The solution that I propose to this previously unsolved problem is based on the Karhunen-Loève expansion of the electric field, and I validate this technique against novel expressions for speckle contrast for different forms of homogeneous field. I also show that this method can readily be extended to cases with spatially varying sample properties, and that it can also be used to model optical and acoustic parameters
