Computational Methods for Image Acquisition and Analysis with Applications in Optical Coherence Tomography

The computational approach to image acquisition and analysis plays an important role in medical imaging and optical coherence tomography (OCT). This thesis is dedicated to the development and evaluation of algorithmic solutions for better image acquisition and analysis with a focus on OCT retinal imaging. For image acquisition, we first developed, implemented, and systematically evaluated a compressive sensing approach for image/signal acquisition for single-pixel camera architectures and an OCT system. Our evaluation outcome provides a detailed insight into implementing compressive data acquisition of those imaging systems. We further proposed a convolutional neural network model, LSHR-Net, as the first deep-learning imaging solution for the single-pixel camera. This method can achieve better accuracy, hardware-efficient image acquisition and reconstruction than the conventional compressive sensing algorithm. Three image analysis methods were proposed to achieve retinal OCT image analysis with high accuracy and robustness. We first proposed a framework for healthy retinal layer segmentation. Our framework consists of several image processing algorithms specifically aimed at segmenting a total of 12 thin retinal cell layers, outperforming other segmentation methods. Furthermore, we proposed two deep-learning-based models to segment retinal oedema lesions in OCT images, with particular attention on processing small-scale datasets. The first model leverages transfer learning to implement oedema segmentation and achieves better accuracy than comparable methods. Based on the meta-learning concept, a second model was designed to be a solution for general medical image segmentation. The results of this work indicate that our model can be applied to retinal OCT images and other small-scale medical image data, such as skin cancer, demonstrated in this thesis

New methods for onset detection of acoustic emission signals

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. The onset time of AE signals detected at different sensors in an array is used to determine their relative time difference of arrival which is essential in performing localisation of the signals’ originating source.Typically, this is done using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques in terms of location accuracy is evaluated using AE signals generated during fatigue crack growth and compared to the established fixed threshold method. It was found that the mean absolute error in performing 1D location using the new methods was between 11.6 to 14.3 mm compared to a range of 19.3 to 37.2 mm for the conventional Fixed Threshold method at different threshold levels

High throughput photonic time stretch optical coherence tomography with data compression

Photonic time stretch enables real time high throughput optical coherence tomography (OCT), but with massive data volume being a real challenge. In this paper, data compression in high throughput optical time stretch OCT has been explored and experimentally demonstrated. This is made possible by exploiting spectral sparsity of encoded optical pulse spectrum using compressive sensing (CS) approach. Both randomization and integration have been implemented in the optical domain avoiding an electronic bottleneck. A data compression ratio of 66% has been achieved in high throughput OCT measurements with 1.51 MHz axial scan rate using greatly reduced data sampling rate of 50 MS/s. Potential to improve compression ratio has been exploited. In addition, using a dual pulse integration method, capability of improving frequency measurement resolution in the proposed system has been demonstrated. A number of optimization algorithms for the reconstruction of the frequency-domain OCT signals have been compared in terms of reconstruction accuracy and efficiency. Our results show that the L1 Magic implementation of the primal-dual interior point method offers the best compromise between accuracy and reconstruction time of the time-stretch OCT signal tested

High throughput photonic time stretch optical coherence tomography with data compression

Photonic time stretch enables real time high throughput optical coherence tomography (OCT), but with massive data volume being a real challenge. In this paper, data compression in high throughput optical time stretch OCT has been explored and experimentally demonstrated. This is made possible by exploiting spectral sparsity of encoded optical pulse spectrum using compressive sensing (CS) approach. Both randomization and integration have been implemented in the optical domain avoiding an electronic bottleneck. A data compression ratio of 66% has been achieved in high throughput OCT measurements with 1.51 MHz axial scan rate using greatly reduced data sampling rate of 50 MS/s. Potential to improve compression ratio has been exploited. In addition, using a dual pulse integration method, capability of improving frequency measurement resolution in the proposed system has been demonstrated. A number of optimization algorithms for the reconstruction of the frequency-domain OCT signals have been compared in terms of reconstruction accuracy and efficiency. Our results show that the L1 Magic implementation of the primal-dual interior point method offers the best compromise between accuracy and reconstruction time of the time-stretch OCT signal tested

Superpixel guided active contour segmentation of retinal layers in OCT volumes

Retinal OCT image segmentation is a precursor to subsequent medical diagnosis by a clinician or machine learning algorithm. In the last decade, many algorithms have been proposed to detect retinal layer boundaries and simplify the image representation. Inspired by the recent success of superpixel methods for pre-processing natural images, we present a novel framework for segmentation of retinal layers in OCT volume data. In our framework, the region of interest (e.g. the fovea) is located using an adaptive-curve method. The cell layer boundaries are then robustly detected firstly using 1D superpixels, applied to A-scans, and then fitting active contours in B-scan images. Thereafter the 3D cell layer surfaces are efficiently segmented from the volume data. The framework was tested on healthy eye data and we show that it is capable of segmenting up to 12 layers. The experimental results imply the effectiveness of proposed method and indicate its robustness to low image resolution and intrinsic speckle noise

LSHR-Net: a hardware-friendly solution for high-resolution computational imaging using a mixed-weights neural network

Recent work showed neural-network based approaches to reconstructing images from compressively sensed measurements offer significant improvements in accuracy and signal compression. Such methods can dramatically boost the capability of computational imaging hardware. However, to date, there have been two major drawbacks: (1) the high-precision real-valued sensing patterns proposed in the majority of existing works can prove problematic when used with computational imaging hardware such as a digital micromirror sampling device and (2) the network structures for image reconstruction involve intensive computation, which is also not suitable for hardware deployment. To address these problems, we propose a novel hardware-friendly solution based on mixed-weights neural networks for computational imaging. In particular, learned binary-weight sensing patterns are tailored to the sampling device. Moreover, we proposed a recursive network structure for low-resolution image sampling and high-resolution reconstruction scheme. It reduces both the required number of measurements and reconstruction computation by operating convolution on small intermediate feature maps. The recursive structure further reduced the model size, making the network more computationally efficient when deployed with the hardware. Our method has been validated on benchmark datasets and achieved state of the art reconstruction accuracy. We tested our proposed network in conjunction with a proof-of-concept hardware setup

Induction of Specific Immune Responses by Severe Acute Respiratory Syndrome Coronavirus Spike DNA Vaccine with or without Interleukin-2 Immunization Using Different Vaccination Routes in Mice▿

DNA vaccines induce humoral and cellular immune responses in animal models and humans. To analyze the immunogenicity of the severe acute respiratory syndrome (SARS) coronavirus (CoV), SARS-CoV, spike DNA vaccine and the immunoregulatory activity of interleukin-2 (IL-2), DNA vaccine plasmids pcDNA-S and pcDNA-IL-2 were constructed and inoculated into BALB/c mice with or without pcDNA-IL-2 by using three different immunization routes (the intramuscular route, electroporation, or the oral route with live attenuated Salmonella enterica serovar Typhimurium). The cellular and humoral immune responses were assessed by enzyme-linked immunosorbent assays, lymphocyte proliferation assays, enzyme-linked immunospot assays, and fluorescence-activated cell sorter analyses. The results showed that specific humoral and cellular immunities could be induced in mice by inoculating them with SARS-CoV spike DNA vaccine alone or by coinoculation with IL-2-expressing plasmids. In addition, the immune response levels in the coinoculation groups were significantly higher than those in groups receiving the spike DNA vaccine alone. The comparison between the three vaccination routes indicated that oral vaccination evoked a vigorous T-cell response and a weak response predominantly with subclass immunoglobulin G2a (IgG2a) antibody. However, intramuscular immunization evoked a vigorous antibody response and a weak T-cell response, and vaccination by electroporation evoked a vigorous response with a predominant subclass IgG1 antibody response and a moderate T-cell response. Our findings show that the spike DNA vaccine has good immunogenicity and can induce specific humoral and cellular immunities in BALB/c mice, while IL-2 plays an immunoadjuvant role and enhances the humoral and cellular immune responses. Different vaccination routes also evoke distinct immune responses. This study provides basic information for the design of DNA vaccines against SARS-CoV