Widefield Computational Biophotonic Imaging for Spatiotemporal Cardiovascular Hemodynamic Monitoring

Cardiovascular disease is the leading cause of mortality, resulting in 17.3 million deaths per year globally. Although cardiovascular disease accounts for approximately 30% of deaths in the United States, many deleterious events can be mitigated or prevented if detected and treated early. Indeed, early intervention and healthier behaviour adoption can reduce the relative risk of first heart attacks by up to 80% compared to those who do not adopt new healthy behaviours. Cardiovascular monitoring is a vital component of disease detection, mitigation, and treatment. The cardiovascular system is an incredibly dynamic system that constantly adapts to internal and external stimuli. Monitoring cardiovascular function and response is vital for disease detection and monitoring. Biophotonic technologies provide unique solutions for cardiovascular assessment and monitoring in naturalistic and clinical settings. These technologies leverage the properties of light as it enters and interacts with the tissue, providing safe and rapid sensing that can be performed in many different environments. Light entering into human tissue undergoes a complex series of absorption and scattering events according to both the illumination and tissue properties. The field of quantitative biomedical optics seeks to quantify physiological processes by analysing the remitted light characteristics relative to the controlled illumination source. Drawing inspiration from contact-based biophotonic sensing technologies such as pulse oximetry and near infrared spectroscopy, we explored the feasibility of widefield hemodynamic assessment using computational biophotonic imaging. Specifically, we investigated the hypothesis that computational biophotonic imaging can assess spatial and temporal properties of pulsatile blood flow across large tissue regions. This thesis presents the design, development, and evaluation of a novel photoplethysmographic imaging system for assessing spatial and temporal hemodynamics in major pulsatile vasculature through the sensing and processing of subtle light intensity fluctuations arising from local changes in blood volume. This system co-integrates methods from biomedical optics, electronic control, and biomedical image and signal processing to enable non-contact widefield hemodynamic assessment over large tissue regions. A biophotonic optical model was developed to quantitatively assess transient blood volume changes in a manner that does not require a priori information about the tissue's absorption and scattering characteristics. A novel automatic blood pulse waveform extraction method was developed to encourage passive monitoring. This spectral-spatial pixel fusion method uses physiological hemodynamic priors to guide a probabilistic framework for learning pixel weights across the scene. Pixels are combined according to their signal weight, resulting in a single waveform. Widefield hemodynamic imaging was assessed in three biomedical applications using the aforementioned developed system. First, spatial vascular distribution was investigated across a sample with highly varying demographics for assessing common pulsatile vascular pathways. Second, non-contact biophotonic assessment of the jugular venous pulse waveform was assessed, demonstrating clinically important information about cardiac contractility function in a manner which is currently assessed through invasive catheterization. Lastly, non-contact biophotonic assessment of cardiac arrhythmia was demonstrated, leveraging the system's ability to extract strong hemodynamic signals for assessing subtle fluctuations in the waveform. This research demonstrates that this novel approach for computational biophotonic hemodynamic imaging offers new cardiovascular monitoring and assessment techniques, which can enable new scientific discoveries and clinical detection related to cardiovascular function

High-Level Intuitive Features (HLIFs) for Melanoma Detection

Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Existing feature sets combine many ad-hoc calculations and are unable to easily provide intuitive diagnostic reasoning. This thesis presents the design and evaluation of a set of features for objectively detecting melanoma in an intuitive and accurate manner. We call these "high-level intuitive features" (HLIFs). The current clinical standard for detecting melanoma, the deadliest form of skin cancer, is visual inspection of the skin's surface. A widely adopted rule for detecting melanoma is the "ABCD" rule, whereby the doctor identifies the presence of Asymmetry, Border irregularity, Colour patterns, and Diameter. The adoption of specialized medical devices for this cause is extremely slow due to the added temporal and financial burden. Therefore, recent research efforts have focused on detection support systems that analyse images acquired with standard consumer-grade camera images of skin lesions. The central benefit of these systems is the provision of technology with low barriers to adoption. Recently proposed skin lesion feature sets have been large sets of low-level features attempting to model the widely adopted ABCD criteria of melanoma. These result in high-dimensional feature spaces, which are computationally expensive and sparse due to the lack of available clinical data. It is difficult to convey diagnostic rationale using these feature sets due to their inherent ad-hoc mathematical nature. This thesis presents and applies a generic framework for designing HLIFs for decision support systems relying on intuitive observations. By definition, a HLIF is designed explicitly to model a human-observable characteristic such that the feature score can be intuited by the user. Thus, along with the classification label, visual rationale can be provided to further support the prediction. This thesis applies the HLIF framework to design 10 HLIFs for skin cancer detection, following the ABCD rule. That is, HLIFs modeling asymmetry, border irregularity, and colour patterns are presented. This thesis evaluates the effectiveness of HLIFs in a standard classification setting. Using publicly-available images obtained in unconstrained environments, the set of HLIFs is compared with and against a recently published low-level feature set. Since the focus is on evaluating the features, illumination correction and manually-defined segmentations are used, along with a linear classification scheme. The promising results indicate that HLIFs capture more relevant information than low-level features, and that concatenating the HLIFs to the low-level feature set results in improved accuracy metrics. Visual intuitive information is provided to indicate the ability of providing intuitive diagnostic reasoning to the user

Intuitive Data-Driven Visualization of Food Relatedness via t-Distributed Stochastic Neighbor Embedding

The relationship between diet and health is important, yet difficultto study in practice. Dietary pattern analysis is one method forinvestigating this link; having more variety in diet tends to be bene-ficial and a score can be generated based on a heuristic approachto food intake habits. We aim to enhance the intuition behindthese food scores by creating an intuitive data-driven visualizationof food relatedness by leveraging t-distributed stochastic neighborembedding (t-SNE). More specifically, by performing t-SNE anal-ysis in a controlled manner to project the high-dimensional nutri-tional information of food items into a lower dimensional food sim-ilarity space, the natural clustering of foods based on the underly-ing nutritional composition becomes visually observable. The effi-cacy of this data-driven approach for visualizing food relatednesswas investigated on a total of 8549 food item entries in the USDAfood composition database, with the results showing considerablepromise as a tool for gaining important nutritional insights. This isthe first step toward providing a novel method to enhance dietarypattern analysis with additional context and insight into food intakehabits based on the inherent nutritional content of the foods con-sumed

A Bayesian Multi-Scale Framework for Photoplethysmogram Imaging Waveform Processing

Photoplethysmography imaging (PPGI) is an increasingly populartechnique for remotely creating signals with a plethora of medicalinformation, referred to as PPGI waveforms. However, PPGI waveformsare often heavily affected by illumination variation and motionartefacts. Current PPGI waveform processing methods are usefulfor estimating heart rate, however, structural detail is not preserved,rendering the signal incapable of providing additional medical information.For this reason, we propose a multi-scale framework basedon the Bayesian residual transform which aims to suppress noiseand preserve structural details necessary for extracting cardiovascularinformation beyond the scope of heart rate. Experiments conductedon a dataset consisting of 24 different PPGI waveforms andcorresponding PPG waveforms captured via a finger pulse oximetersuggests a high level of noise and ambient illumination variationsuppression is achieved while signal fidelity is largely retained

Co-integrating thermal and hemodynamic imaging for physiological monitoring

Photoplethysmographic imaging (PPGI) has gained popularity fornon-intrusive cardiovascular monitoring. However, certain symptoms(e.g., fever) may not be easily detectable using cardiovascularbiomarkers. Here, we investigate the co-integration of PPGIand thermal imaging to create a non-contact, widefield, multimodalphysiological monitoring system. To achieve strong PPGI performance,high-power infrared LED stability was investigated by evaluatingtwo LED driver boards. Results show that the multimodalimaging system was able to acquire spatially consistent hemodynamicpulsatility and heat distributions in a case study. This multimodalsystem may lead to improved systemic disease detectionand monitoring

Integrating Multispectral Hemodynamic Imaging for Bulk Tissue Oxygenation Analysis

Tissue perfusion and oxygenation are important factors in predicting patient outcomes, but current non-invasive devices for this type of measurement are limited to contact-based single-site monitoring. We present the co-integration of a multispectral optical-electronic subsystem into an existing non-contact coded hemodynamic imaging (CHI) device to enable image acquisition under different illuminants for spatial tissue oxygenation. Stability of the optical output for three illuminants over 10 mins was validated by the imaging system, with σmax=0.407 intensity units, reflecting stability in local fluctuations, and a maximal overall change of 3.1 units. Bulk tissue oxygenation measurement of the thenar eminence during a cuff occlusion experiment revealed relative changes in absorbance due to oxy- and deoxyhemoglobin consistent with concurrent physiological changes in chromophore concentration as described in a previous study