Purpose: The RTVue-100 is a new instrument for measuring retinal blood flow (RBF), but image quality needs to be optimized in order for valid blood flow results. The primary aim of this thesis was to assess the presence of learning effects with novice and experienced operators. Methods: Twelve upper-year optometric students from the University of Waterloo, School of Optometry and Vision Science, were trained in operating RTVue-100. Nine healthy participants, with a mean age (?? SD) of 25.7 ?? 3.8 years, underwent OCT imaging. Using the Doppler OCT of Retinal Circulation (DOCTORC) software, images were assessed by computer for various image quality parameters. Results: Paired samples t-tests showed significant statistical differences between the novice and experienced operators for the following image acquisition parameters: total acquisition time (TAT), number of attempts to complete total scan protocol, and number of valid images. Mean values for TAT and the number of attempts decreased, whereas the mean number of valid images increased from novice to experienced level. Conclusions: The results confirm that there are learning effects observed within the image acquisition process using the RTVue-100 SD-OCT.

In spectral domain optical coherence tomography (SD-OCT), any transverse motion component of a detected obliquely moving sample results in a nonlinear relationship between the Doppler phase shift and the axial sample velocity restricting phase-resolved Doppler OCT. To circumvent the limitation, we propose the lateral resonant Doppler flow quantification in spectral domain OCT, where the scanner movement velocity is matched to the transverse velocity component of the sample motion.

The task of automatically segmenting 3-D surfaces representing object boundaries is important in quantitative analysis of volumetric images, which plays a vital role in numerous biomedical applications. For the diagnosis and management of disease, segmentation of images of organs and tissues is a crucial step for the quantification of medical images. Segmentation finds the boundaries or, limited to the 3-D case, the surfaces, that separate regions, tissues or areas of an image, and it is essential that these boundaries approximate the true boundary, typically by human experts, as closely as possible. Recently, graph-based methods with a global optimization property have been studied and used for various applications. Sepecifically, the state-of-the-art graph search (optimal surface segmentation) method has been successfully used for various such biomedical applications. Despite their widespread use for image segmentation, real world medical image segmentation problems often pose difficult challenges, wherein graph based segmentation methods in its purest form may not be able to perform the segmentation task successfully. This doctoral work has a twofold objective. 1)To identify medical image segmentation problems which are difficult to solve using existing graph based method and develop novel methods by employing graph search as a building block to improve segmentation accuracy and efficiency. 2) To develop a novel multiple surface segmentation strategy using deep learning which is more computationally efficient and generic than the exisiting graph based methods, while eliminating the need for human expert intervention as required in the current surface segmentation methods. This developed method is possibly the first of its kind where the method does not require and human expert designed operations. To accomplish the objectives of this thesis work, a comprehensive framework of graph based and deep learning methods is proposed to achieve the goal by successfully fulfilling the follwoing three aims. First, an efficient, automated and accurate graph based method is developed to segment surfaces which have steep change in surface profiles and abrupt distance changes between two adjacent surfaces. The developed method is applied and validated on intra-retinal layer segmentation of Spectral Domain Optical Coherence Tomograph (SD-OCT) images of eye with Glaucoma, Age Related Macular Degneration and Pigment Epithelium Detachment. Second, a globally optimal graph based method is developed to attain subvoxel and super resolution accuracy for multiple surface segmentation problem while imposing convex constraints. The developed method was applied to layer segmentation of SD-OCT images of normal eye and vessel walls in Intravascular Ultrasound (IVUS) images. Third, a deep learning based multiple surface segmentation is developed which is more generic, computaionally effieient and eliminates the requirement of human expert interventions (like transformation designs, feature extrraction, parameter tuning, constraint modelling etc.) required by existing surface segmentation methods in varying capacities. The developed method was applied to SD-OCT images of normal and diseased eyes, to validate the superior segmentaion performance, computation efficieny and the generic nature of the framework, compared to the state-of-the-art graph search method.

Laser processing has the advantage of minimal sample contact and thus little tool wear over time compared to conventional machining. However, this leads to the difficulty of real-time depth monitoring and control. To help understand the process and achieve automation of laser micromachining, a coherent imaging technique adapted from spectral domain optical coherence tomography (SD-OCT) is applied “inline”with a machining laser to monitor the depth changing information. The axial resolution of the inline coherent imaging (ICI) system is 7–8 microns and the acquisition rate is up to 230 kHz. The capture time is as low as 1.5 microseconds. 3D laser machining usually requires ultrafast lasers and homogeneous materials. With ICI, a feedback system is developed for 3D sculpture suitable even for heterogeneous materials without any sophisticated material characterization. 3D patterns with sizes up to 1 mm × 1 mm × 0.2 mm are sculpted in bone and wood with a ps UV laser. 3D patterns with sizes up to 6 mm × 6 mm × 2 mm are sculpted in bone with a CW IR laser. Many laser applications require high scan speed facilitated by scanning optics. The versatility of ICI is also demonstrated in a galvo-telecentric beam delivery system. ICI is used in a process of trench (as long as 10 mm) etching of steel to monitor the intrapulse and interpulse morphology changes as well as the sweep-to-sweep (up to 36 sweeps) depth changes. High scan speed (up to 375 mm/s) trench etching of silicon are also monitored and the parameter space is explored without destructive post-processing. Motion during imaging capture time (>1.5 microseconds) may cause contrast degradation. To reduce the motion artifacts, preliminary experiments on stroboscopic ICI based on a kHz pulse repetition rate femtosecond laser are described. By “sampling” the motion of the machining front discretely with a “sampling time” as short as the imaging pulse duration, our results demonstrate that stroboscopic ICI is a promising way to improve the ICI contrast against motion artifacts. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2014-04-30 13:56:35.793