<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>

No description available.

<p>    </p> <p>Cerebrovascular diseases, such as stroke, constitute the most common life-threatening neurological disease in the United States. To support normal brain function, maintaining adequate brain perfusion (i.e., cerebral blood flow (CBF)) is important. Therefore, it is crucial to assess the brain perfusion so that early intervention in cerebrovascular diseases can be applied if abnormal perfusion is observed. The goal of my study is to develop metrics to measure the brain perfusion through modeling brain physiology using resting-state and task-based blood-oxygenation-level- dependent (BOLD) functional MRI (fMRI). My first and second chapters focused on deriving the blood arrival time using the resting-state BOLD signal. In the first chapters, we extracted the systemic low-frequency oscillations (sLFOs) in the fMRI signal from the internal carotid arteries (ICA) and the superior sagittal sinus (SSS). Consistent and robust results were obtained across 400 scans showing the ICA signals leading the SSS signals by about 5 seconds. This delay time could be considered as an effective perfusion biomarker that is associate with the cerebral circulation time (CCT). To further explore sLFOs in assessing dynamic blood flow changes during the scan, in my second chapter, a “carpet plot” (a 2-dimensional plot time vs. voxel) of scaled fMRI signal intensity was reconstructed and paired with a developed slope-detection algorithm. Tilted vertical edges across which a sudden signal intensity change took place were successfully detected by the algorithm and the averaged propagation time derived from the carpet plot matches the cerebral circulation time. Given that CO₂ is a vasodilator, controlling of inhaled CO₂ is able to modulate the BOLD signal, therefore, as a follow-up study, we focused on investigating the feasibility of using a CO₂ modulated sLFO signal as a “natural” bolus to track CBF with the tool developed from the second chapter. Meaningful transit times were derived from the CO₂-MRI carpet plots. Not only the timing, the BOLD signal deformation (the waveform change) under CO₂ challenge also reveals very useful perfusion information, representing how the brain react to stimulus. Therefore, my fourth chapter focused on characterizing the brain reaction to the CO₂ stimulus to better measure the brain health using BOLD fMRI. Overall, these studies deepen our understanding of fMRI signal and the derived perfusion parameters can potentially be used to assess some cerebrovascular diseases, such as stroke, ischemic brain damage, and steno-occlusive arterial disease in addition to functional activations. </p>

<p dir="ltr">Fibromuscular Dysplasia (FMD) is a non-inflammatory, non-atherosclerotic blood vessel disorder characterized by a series of narrowed and dilated regions of vasculature. These patients are prescribed blood thinners or anti-platelet therapeutics as treatment to this systemic disease. Current image-based diagnostic methods cannot reliably predict a patient’s risk of stroke in order to properly manage medication. There are also challenges in distinguishing FMD from other diseases that can cause arterial obstructions, like atherosclerosis or vasculitis.</p><p dir="ltr">The ultimate goal of this research is to develop a methodology for evaluating the risk of mechanical platelet activation based on medical imaging. Our hypothesis is that subject-specific assessment of platelet activation due to hemodynamic stress can improve risk stratification of FMD patients. The aims of the projects were therefore to 1) Develop a CFD-based methodology for estimating platelet activation state, and 2) Test this methodology on a small cohort of subjects with FMD, carotid artery stenosis, and healthy controls. A modeling workflow was developed, combining Eulerian and Lagrangian approaches to compute flow fields and evaluate shear stress history of particles advected through the vascular geometries. From this stress history, predictive estimates of mechanical platelet activation can be calculated utilizing a platelet activation state (PAS) metric. We applied this modeling workflow to assess platelet activation in segments of carotid arteries of patients with Fibromuscular Dysplasia, Carotid Artery Stenosis, and healthy controls for comparison against experiments performed at the Cleveland Clinic assessing mechanical platelet activation in patients with each of these conditions. This work supports the development of a patient-specific determination of these same metrics, in order to more precisely assess patient risk of stroke.</p>

Diabetes is a rapidly increasing health problem. In a global perspective,approximately 415 million people suffered from diabetes in 2015 and this number ispredicted to increase to 640 million by 2040. To tackle this pandemic there is a needfor better analytical tools by which we can increase our understanding of the disease.One discipline that has already provided much needed insight to diabetes etiology isoptical molecular imaging. Using various forms of light it is possible to create animage of the analysed sample that can provide information about molecularmechanistic aspects of the disease and to follow spatial and temporal dynamics. The overall aim of this thesis is to improve and adapt existing andnovel optical imaging approaches for their specific use in diabetes research. Hereby,we have focused on three techniques: (I) Optical projection tomography (OPT),which can be described as the optical equivalent of x-ray computed tomography(CT), and two vibrational microspectroscopic (VMS) techniques, which records theunique vibrational signatures of molecules building up the sample: (II) Fouriertransforminfrared vibrational microspectroscopy (FT-IR) and (III) Ramanvibrational microspectroscopy (Raman). The computational tools and hardware applications presented here generallyimprove OPT data quality, processing speed, sample size and channel capacity.Jointly, these developments enable OPT as a routine tool in diabetes research,facilitating aspects of e.g. pancreatic β-cell generation, proliferation,reprogramming, destruction and preservation to be studied throughout the pancreaticvolume and in large cohorts of experimental animals. Further, a novel application ofmultivariate analysis of VMS data derived from pancreatic tissues is introduced.This approach enables detection of novel biochemical alterations in the pancreasduring diabetes disease progression and can be used to confirm previously reportedbiochemical alterations, but at an earlier stage. Finally, our studies indicate thatRaman imaging is applicable to in vivo studies of grafted islets of Langerhans,allowing for longitudinal studies of pancreatic islet biochemistry.viIn summary, presented here are new and improved methods by which opticalimaging techniques can be utilised to study 3D-spatial, quantitative andmolecular/biochemical alterations of the normal and diseased pancreas.

Chronic obstructive pulmonary disease (COPD) describes a range of lung conditions including emphysema, chronic bronchitis and small airways disease. While COPD is a major cause of death and debilitating illness, current clinical assessment methods are inadequate: they are a poor predictor of patient outcome and insensitive to mild disease. A new imaging technology, hyperpolarised xenon MRI, offers the hope of improved diagnostic techniques, based on regional measurements using functional imaging. There is a need for quantitative analysis techniques to assist in the interpretation of these images. The aim of this work is to develop these techniques as part of a clinical trial into hyperpolarised xenon MRI. In this thesis we develop a fully automated pipeline for deriving regional measurements of lung function, making use of the multiple imaging modalities available from the trial. The core of our pipeline is a novel method for automatically segmenting the pulmonary lobes from CT data. This method combines a Hessian-based filter for detecting pulmonary fissures with anatomical cues from segmented lungs, airways and pulmonary vessels. The pipeline also includes methods for segmenting the lungs from CT and MRI data, and the airways from CT data. We apply this lobar map to the xenon MRI data using a multi-modal image registration technique based on automatically segmented lung boundaries, using proton MRI as an intermediate stage. We demonstrate our pipeline by deriving lobar measurements of ventilated volumes and diffusion from hyperpolarised xenon MRI data. In future work, we will use the trial data to further validate the pipeline and investigate the potential of xenon MRI in the clinical assessment of COPD. We also demonstrate how our work can be extended to build personalised computational models of the lung, which can be used to gain insights into the mechanisms of lung disease.

The focus of the thesis is on the development of a few stochastic search schemes for inverse problems and their applications in medical imaging. After the introduction in Chapter 1 that motivates and puts in perspective the work done in later chapters, the main body of the thesis may be viewed as composed of two parts: while the first part concerns the development of stochastic search algorithms for inverse problems (Chapters 2 and 3), the second part elucidates on the applicability of search schemes to inverse problems of interest in tomographic imaging (Chapters 4 and 5). The chapter-wise contributions of the thesis are summarized below. Chapter 2 proposes a Monte Carlo stochastic filtering algorithm for the recursive estimation of diffusive processes in linear/nonlinear dynamical systems that modulate the instantaneous rates of Poisson measurements. The same scheme is applicable when the set of partial and noisy measurements are of a diffusive nature. A key aspect of our development here is the filter-update scheme, derived from an ensemble approximation of the time-discretized nonlinear Kushner Stratonovich equation, that is modified to account for Poisson-type measurements. Specifically, the additive update through a gain-like correction term, empirically approximated from the innovation integral in the filtering equation, eliminates the problem of particle collapse encountered in many conventional particle filters that adopt weight-based updates. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth, first with application to filtering problems with diffusive or Poisson-type measurements and then to an automatic control problem wherein the exterminations of the associated cost functional is achieved simply by an appropriate redefinition of the innovation process. The aim of one of the numerical examples in Chapter 2 is to minimize the structural response of a duffing oscillator under external forcing. We pose this problem of active control within a filtering framework wherein the goal is to estimate the control force that minimizes an appropriately chosen performance index. We employ the proposed filtering algorithm to estimate the control force and the oscillator displacements and velocities that are minimized as a result of the application of the control force. While Fig. 1 shows the time histories of the uncontrolled and controlled displacements and velocities of the oscillator, a plot of the estimated control force against the external force applied is given in Fig. 2. (a) (b) Fig. 1. A plot of the time histories of the uncontrolled and controlled (a) displacements and (b) velocities. Fig. 2. A plot of the time histories of the external force and the estimated control force Stochastic filtering, despite its numerous applications, amounts only to a directed search and is best suited for inverse problems and optimization problems with unimodal solutions. In view of general optimization problems involving multimodal objective functions with a priori unknown optima, filtering, similar to a regularized Gauss-Newton (GN) method, may only serve as a local (or quasi-local) search. In Chapter 3, therefore, we propose a stochastic search (SS) scheme that whilst maintaining the basic structure of a filtered martingale problem, also incorporates randomization techniques such as scrambling and blending, which are meant to aid in avoiding the so-called local traps. The key contribution of this chapter is the introduction of yet another technique, termed as the state space splitting (3S) which is a paradigm based on the principle of divide-and-conquer. The 3S technique, incorporated within the optimization scheme, offers a better assimilation of measurements and is found to outperform filtering in the context of quantitative photoacoustic tomography (PAT) to recover the optical absorption field from sparsely available PAT data using a bare minimum ensemble. Other than that, the proposed scheme is numerically shown to be better than or at least as good as CMA-ES (covariance matrix adaptation evolution strategies), one of the best performing optimization schemes in minimizing a set of benchmark functions. Table 1 gives the comparative performance of the proposed scheme and CMA-ES in minimizing a set of 40-dimensional functions (F1-F20), all of which have their global minimum at 0, using an ensemble size of 20. Here, 10 5 is the tolerance limit to be attained for the objective function value and MAX is the maximum number of iterations permissible to the optimization scheme to arrive at the global minimum. Table 1. Performance of the SS scheme and Chapter 4 gathers numerical and experimental evidence to support our conjecture in the previous chapters that even a quasi-local search (afforded, for instance, by the filtered martingale problem) is generally superior to a regularized GN method in solving inverse problems. Specifically, in this chapter, we solve the inverse problems of ultrasound modulated optical tomography (UMOT) and diffraction tomography (DT). In UMOT, we perform a spatially resolved recovery of the mean-squared displacements, p r of the scattering centres in a diffusive object by measuring the modulation depth in the decaying autocorrelation of the incident coherent light. This modulation is induced by the input ultrasound focussed to a specific region referred to as the region of interest (ROI) in the object. Since the ultrasound-induced displacements are a measure of the material stiffness, in principle, UMOT can be applied for the early diagnosis of cancer in soft tissues. In DT, on the other hand, we recover the real refractive index distribution, n r of an optical fiber from experimentally acquired transmitted intensity of light traversing through it. In both cases, the filtering step encoded within the optimization scheme recovers superior reconstruction images vis-à-vis the GN method in terms of quantitative accuracies. Fig. 3 gives a comparative cross-sectional plot through the centre of the reference and reconstructed p r images in UMOT when the ROI is at the centre of the object. Here, the anomaly is presented as an increase in the displacements and is at the centre of the ROI. Fig. 4 shows the comparative cross-sectional plot of the reference and reconstructed refractive index distributions, n r of the optical fiber in DT. Fig. 3. Cross-sectional plot through the center of the reference and reconstructed p r images. Fig. 4. Cross-sectional plot through the center of the reference and reconstructed n r distributions. In Chapter 5, the SS scheme is applied to our main application, viz. photoacoustic tomography (PAT) for the recovery of the absorbed energy map, the optical absorption coefficient and the chromophore concentrations in soft tissues. Nevertheless, the main contribution of this chapter is to provide a single-step method for the recovery of the optical absorption field from both simulated and experimental time-domain PAT data. A single-step direct recovery is shown to yield better reconstruction than the generally adopted two-step method for quantitative PAT. Such a quantitative reconstruction maybe converted to a functional image through a linear map. Alternatively, one could also perform a one-step recovery of the chromophore concentrations from the boundary pressure, as shown using simulated data in this chapter. Being a Monte Carlo scheme, the SS scheme is highly parallelizable and the availability of such a machine-ready inversion scheme should finally enable PAT to emerge as a clinical tool in medical diagnostics. Given below in Fig. 5 is a comparison of the optical absorption map of the Shepp-Logan phantom with the reconstruction obtained as a result of a direct (1-step) recovery. Fig. 5. The (a) exact and (b) reconstructed optical absorption maps of the Shepp-Logan phantom. The x- and y-axes are in m and the colormap is in mm-1. Chapter 6 concludes the work with a brief summary of the results obtained and suggestions for future exploration of some of the schemes and applications described in this thesis.

<p dir="ltr">People suffering from COPD are also known to suffer from other musculoskeletal issues like fracture risk, back pain, etc. Intervertebral disc degeneration (IVD) is a prominent cause of back pain and inflammation, influenced by factors such as aging, sudden loading, and genetics. <i>SERPINA1</i>, a common genetic variant in individuals with chronic obstructive pulmonary disease (COPD), encodes the alpha-antitrypsin protein (AAT). AAT deficiency is also associated with IVD degeneration, bone loss, and gait impairment. Currently, AAT-deficient individuals receive costly and short-lived weekly AAT injections, with no established guidelines for managing IVD degeneration and osteoporosis. Our primary research objective was to examine the effects of <i>serpinA1a/c</i> using a mouse model with global knockout (KO) of <i>serpinA1a/c</i>, generated through CRISPR technology, on intervertebral discs (IVD) and bone. We found that global deletion of <i>serpinA1a/c</i> was found to cause IVD elastin degradation, leading to a loss of mechanical properties. Moreover, <i>serpinA1</i> was associated with increased bone-resorbing cells (osteoclasts) and a reduction in bone-forming cells (osteoblasts). Notably, sexual dimorphism was observed, with female IVDs exhibiting less degeneration than male counterparts, and <i>serpinA1a/c</i> KO mice were protected from mechanically-induced tail compression. Even in human IVDs, males expressed more AAT-1 compared to female IVDs. There are no FDA-approved drugs currently existing for IVD degeneration. Since IVD degeneration frequently occurs in individuals with osteoporosis, it shows a probable cross-talk happening between IVD and bone. In our study, we found the association of <i>serpinA1 </i>with estrogen receptor alpha and osteoclasts. Hence, we investigated the potential of raloxifene, an FDA-approved selective estrogen receptor modulator (SERM) typically prescribed to post-menopausal women for osteoporosis treatment, in averting IVD degeneration and improving mechanical characteristics in IVD. Our findings suggest that raloxifene injection may retard IVD degeneration induced by AAT deficiency, particularly in male mice. Furthermore, the latter study touched upon a conditional <i>serpinA1a</i> mouse model crossed with aggrecan-cre, specifically targeting <i>serpinA1a</i>-expressing cells in the IVD while sparing bone. Conditional <i>serpinA1a</i> deletion induced mild IVD degeneration without affecting bone loss. In summary, this study serves as a foundation for testing potential treatments for AAT patients with IVD degeneration and osteoporosis. It also provides compelling evidence for considering raloxifene as a treatment option for IVD degeneration in AAT-deficient patients experiencing IVD-related pain.</p>