Modelling Light Transport Through Biological Tissue Using the Simplified Spherical Harmonics Approximation

Optical Tomography is a medical imaging modality that can be used to non- invasively image functional changes within the body. As near-infrared light is highly scattered by biological tissue, the process of image reconstruction is ill-posed and, in general is also under-determined. As such, model based iterative image reconstruction methods are used. These methods require an accurate model of light propagation through tissue, also known as the forward model. The diffusion approximation (DA) to the radiative transport equation is one of the most widely used forward models. It is based on the assumption that scattering events dominate over absorption events resulting in a diffuse light distribution. This is valid in cases with low absorption coefficients or large geometries (greater than a few scattering lengths). In many cases, however, such as in small animal imaging where the source-detector separation is small, this assumption is not valid and so a higher-ordered approximation is required. In this thesis, a three-dimensional frequency domain forward model based on the simplified spherical harmonics (SPN) approximation to the radiative transport equation is introduced. By comparison with a Monte- Carlo model, the SPN approximation is shown to be more accurate than the DA, especially in regions near to the sources and detectors and the increase in accuracy is greater in cases with stronger absorption. This is particularly important for bioluminescent imaging of small animals which involve both small geometries and strong absorption. Due to the asymptotic nature of the 3 SPN approximation, the highest ordered model was not necessarily the most accurate, but all models with N>1 were more accurate than the DA. The SPN based forward model has also been implemented into an image reconstruction algorithm. Despite the fact that the SPN approximation does not combine the scattering coefficient and anisotropy factor into a single variable, as is the case in the DA, it was found that it is not possible to reconstruct them uniquely. The SPN based models were shown to be able to reconstruct optical maps with greater accuracy than the DA. However, due to the increased number of unknowns to be recovered, the SP7 based reconstructed images contained significant artefact and cross-talk. Finally, a SPN-Diffusion hybrid model was developed in which the SPN model was used in the regions near to the source and the DA elsewhere. This model provides the increase of accuracy of the SPN models in the regions where the DA is insufficient, whilst retaining the computational efficiency of the DA. It was shown that the hybrid model leads to increased accuracy not only in the regions solved using the SPN model, but also in the DA based regions where as in a pure DA model, the errors near the source were propagated throughout the domain. It is also shown that the hybrid model can be solved in half the time of the full SPN model

Techniques basées sur des modèles et apprentissage machine pour la reconstruction d’image non-linéaire en tomographie optique diffuse

La tomographie optique diffuse (TOD) est une modalité d’imagerie biomédicale 3D peu dispendieuse et non-invasive qui permet de reconstruire les propriétés optiques d’un tissu biologique. Le processus de reconstruction d’images en TOD est difficile à réaliser puisqu’il nécessite de résoudre un problème non-linéaire et mal posé. Les propriétés optiques sont calculées à partir des mesures de surface du milieu à l’étude. Dans ce projet, deux méthodes de reconstruction non-linéaire pour la TOD ont été développées. La première méthode utilise un modèle itératif, une approche encore en développement qu’on retrouve dans la littérature. L’approximation de la diffusion est le modèle utilisé pour résoudre le problème direct. Par ailleurs, la reconstruction d’image à été réalisée dans différents régimes, continu et temporel, avec des mesures intrinsèques et de fluorescence. Dans un premier temps, un algorithme de reconstruction en régime continu et utilisant des mesures multispectrales est développé pour reconstruire la concentration des chromophores qui se trouve dans différents types de tissus. Dans un second temps, un algorithme de reconstruction est développé pour calculer le temps de vie de différents marqueurs fluorescents à partir de mesures optiques dans le domaine temporel. Une approche innovatrice a été d’utiliser la totalité de l’information du signal temporel dans le but d’améliorer la reconstruction d’image. Par ailleurs, cet algorithme permettrait de distinguer plus de trois temps de vie, ce qui n’a pas encore été démontré en imagerie de fluorescence. La deuxième méthode qui a été développée utilise l’apprentissage machine et plus spécifiquement l’apprentissage profond. Un modèle d’apprentissage profond génératif est mis en place pour reconstruire la distribution de sources d’émissions de fluorescence à partir de mesures en régime continu. Il s’agit de la première utilisation d’un algorithme d’apprentissage profond appliqué à la reconstruction d’images en TOD de fluorescence. La validation de la méthode est réalisée avec une mire aux propriétés optiques connues dans laquelle sont inséres des marqueurs fluorescents. La robustesse de cette méthode est démontrée même dans les situations où le nombre de mesures est limité et en présence de bruit.Abstract : Diffuse optical tomography (DOT) is a low cost and noninvasive 3D biomedical imaging technique to reconstruct the optical properties of biological tissues. Image reconstruction in DOT is inherently a difficult problem, because the inversion process is nonlinear and ill-posed. During DOT image reconstruction, the optical properties of the medium are recovered from the boundary measurements at the surface of the medium. In this work, two approaches are proposed for non-linear DOT image reconstruction. The first approach relies on the use of iterative model-based image reconstruction, which is still under development for DOT and that can be found in the literature. A 3D forward model is developed based on the diffusion equation, which is an approximation of the radiative transfer equation. The forward model developed can simulate light propagation in complex geometries. Additionally, the forward model is developed to deal with different types of optical data such as continuous-wave (CW) and time-domain (TD) data for both intrinsic and fluorescence signals. First, a multispectral image reconstruction algorithm is developed to reconstruct the concentration of different tissue chromophores simultaneously from a set of CW measurements at different wavelengths. A second image reconstruction algorithm is developed to reconstruct the fluorescence lifetime (FLT) of different fluorescent markers from time-domain fluorescence measurements. In this algorithm, all the information contained in full temporal curves is used along with an acceleration technique to render the algorithm of practical use. Moreover, the proposed algorithm has the potential of being able to distinguish more than 3 FLTs, which is a first in fluorescence imaging. The second approach is based on machine learning techniques, in particular deep learning models. A deep generative model is proposed to reconstruct the fluorescence distribution map from CW fluorescence measurements. It is the first time that such a model is applied for fluorescence DOT image reconstruction. The performance of the proposed algorithm is validated with an optical phantom and a fluorescent marker. The proposed algorithm recovers the fluorescence distribution even from very noisy and sparse measurements, which is a big limitation in fluorescence DOT imaging

Molecular interpretation of pharmaceuticals' adsorption on carbon nanomaterials: Theory meets experiments

The ability of carbon-based nanomaterials (CNM) to interact with a variety of pharmaceutical drugs can be exploited in many applications. In particular, they have been studied both as carriers for in vivo drug delivery and as sorbents for the treatment of water polluted by pharmaceuticals. In recent years, the large number of experimental studies was also assisted by computational work as a tool to provide understanding at molecular level of structural and thermodynamic aspects of adsorption processes. Quantum mechanical methods, especially based on density functional theory (DFT) and classical molecular dynamics (MD) simulations were mainly applied to study adsorption/release of various drugs. This review aims to compare results obtained by theory and experiments, focusing on the adsorption of three classes of compounds: (i) simple organic model molecules; (ii) antimicrobials; (iii) cytostatics. Generally, a good agreement between experimental data (e.g. energies of adsorption, spectroscopic properties, adsorption isotherms, type of interactions, emerged from this review) and theoretical results can be reached, provided that a selection of the correct level of theory is performed. Computational studies are shown to be a valuable tool for investigating such systems and ultimately provide useful insights to guide CNMs materials development and design

Master of Science

thesisTraumatic brain injury (TBI) is a leading cause of death and disability in the U.S.A. In mild cases, common etiologies of TBI (i.e., hemorrhage or edema) are not readily apparent during medical examination. We propose that the pia-arachnoid complex (PAC) contributes to the brain's response in TBI. The PAC is the only layer of tissue between the brain and dura (a tough membrane tightly adhered to the skull), and acts as a mechanical tether between the brain and skull. If the fine structures of the PAC are damaged during TBI, they likely go undiagnosed due to their small size and difficulty to image. To better understand the mechanics of PAC injury, several experimental and computational studies were conducted. First, a novel application of optical coherence tomography (OCT) was utilized to acquire microscale images of the in-situ porcine PAC and measure the amount of arachnoid trabeculae (AT) present. Next, two parametric studies were conducted on a microscale model of the PAC which evaluated its sensitivity to variable substructure moduli and AT volume fraction (VF). Afterwards, the microscale PAC model was paired with a macroscale head model to determine the effect of a nonuniform AT VF on whole-head mechanics. Finally, an immature porcine model of mild TBI was used to investigate PAC damage following head rotation, and identify clinically relevant MRI biomarkers indicative of PAC damage. The OCT imaging of the PAC revealed high variability of VF within each head, but low variability between brain regions and between animals. The microscale parametric studies showed high sensitivity to changes in substructure moduli and VF. The macroscale model studies showed improvement of intracranial hemorrhage prediction when variable VF was introduced into the models. Clinically relevant biomarkers of PAC damage were not able to be confidently developed, but increased sample size and improved resolution may lead to innovative biomarkers for TBI. The work presented here addresses a significant lack of data on the PAC, and presents new insights into its anatomy and biomechanics. Many of the studies presented here are the first of their kind, opening up many new paths of TBI research opportunities

Synthesis and Evaluation of a Novel Polymer Microfiber Drug Delivery System

Skin cancer is the most prevalent cancer diagnosis worldwide. Squamous cell carcinoma (SCC) is one of the most common diagnoses. Fortunately, these cancers are rarely fatal if detected and treated early on. However, current treatment options can be painful, disfiguring and can require long-term treatment courses, resulting in poor patient compliance and cancer progression. Since SCC begins as precancerous lesions, an opportunity exists for early preventative interventions which this work aims to address. We produced stabilized microfibers via centrifugal spinning and UV photocrosslinking composed of poly(ethylene oxide) functionalized with cinnamoyl chloride. Curcumin, a molecule known for its anti-cancer properties was loaded into the stabilized fibers and exhibited sustained release. The dose-dependent effect of free curcumin on A549 cancer cells was investigated. This work demonstrates the potential for this system as a transdermal delivery device for the treatment of skin cancer

The impact of carbon based materials on hippocampal cells: from neurons to networks.

Tissue engineering and regenerative medicine require the constant development of synthetic materials to manufacture scaffolds thatbetter integrate into the target tissues (O\u2019Brien, 2011; Ku et al, 2013; Harrison et al, 2014). In this framework, newly synthesized nanomaterials made of pure carbon, in particular Carbon Nanotubes (Ijima, 1991) and Graphene (Novoselov et al, 2004) applications to biology received particular attention due to their outstanding physicochemical properties (Hirsch, 2010). Our team has performed pioneer works during the last decade, about the interactions of neural cells with carbon nanotubes (Lovat et al, 2005; Mazzatenta et al, 2007; Cellot et al, 2009; Cellot et al, 2011; Fabbro et al, 2012; Bosi et al, 2015), and with graphene (Fabbro et al, 2015; Rauti et al, 2016) or, more in general, with synthetic substrates (Cellot et al, 2016). The major aim of my work has been to use traditional and novel physiology tools to investigate further these \u201cneuro-hybrid systems\u201d, and to understand how far Carbon Nanotubes and Graphene can be pushed in neuroscience applications. With this aim, in the first part of my PhD I further elucidated the behavior of newly formed synapses in primary dissociated neurons when interfaced to bi-dimensional substrates of Multi-walled Carbon Nanotubes. I then addressed the homeostasis of invitro neural networks interfaced to pure graphene and I characterized for the first time the changes induced by this material in neurons. As last step, I set up a more complex biological in-vitro model, consisting of lesioned organotypic Entorhinal-Hippocampal cultures (Perederiy and Westbrook, 2013) and we described the regenerative features of Carbon Nanotubes in this lesion model. During my PhD I was also involved in two side projects: in the first one, in collaboration with Sebastian Reinhartz and Matthew Diamond (SISSA), we refine the possible approaches of the optogenetic technique, by manipulating neuronal responses with different light waveforms (Reinhartz et al, MS in preparation, in the appendix). In the second one, in collaboration with the group of Manus Biggs, from the National University of Galway, Ireland, we tested the biocompatibility and addressed the neural behavior of primary neural cells interfaced with Indium Tin Oxide (ITO) substrates with different roughness, thickness and conducting profiles (Vallejo-Giraldo et al, 2017)

Pseudo-Random Single Photon Counting for Time-Resolved Optical Measurements

Ph.DDOCTOR OF PHILOSOPH

Dynamic Digital Optical Tomography for Cancer Imaging and Therapy Monitoring

Diffuse optical tomography is a non-invasive imaging technique that uses near-infrared light to create three-dimensional images of tissue. This dissertation presents the design and validation of an instrument for rapid optical imaging using digital detection techniques. In addition to a detailed description of the instrument, three studies are presented: a clinical study detecting breast cancer using dynamic optical imaging; a pre-clinical study monitoring early tumor response to anti-angiogenic therapy; and a clinical study monitoring individual patient response to neoadjuvant chemotherapy. These studies show that diffuse optical tomography is a valuable imaging modality that can play an important role in cancer detection and treatment

A dark field illumination probe linked to Raman spectroscopy for non-invasivety determination of ocular biomarkers

For early and effective diagnosis of eye diseases, acquiring biochemical information in the eye is preferred. However, it is obtained by performing a biopsy of the eye tissue. This poses a risk to the integrity of the eye and cannot be performed on a regular basis. Raman spectrometry is a potential and powerful tool for the non-invasive investigation of biochemical information. The challenge to use it in an ophthalmic application is the essential of a high-power laser direct shining through the eye, which raises safety concerns for potential retinal damage .In this thesis, biomedical applications of Raman spectroscopy are explored for eye disease biomarkers and ocular drug measurements in ex vitro, in vitro and in vivo. To ensure a safety measurement by projecting a laser in the eye, two types of dark-field illumination probes are designed, manufactured and validated in conjunction with confocal Raman spectroscopy (CRS) to avoid light damage of the retina. Furthermore, a non-contact dark-field illumination method for the same purpose is proposed and theoretically validated

Atherosclerotic Plaque Characterization in Humans with Acoustic Radiation Force Impulse (ARFI) Imaging

Cardio- and cerebrovascular diseases (CVD) are among the leading causes of death and disability in the United States. A vast majority of heart attacks and strokes are linked to atherosclerosis; a condition characterized by inflammation and plaque accumulation in the arterial wall that can rupture and propagate an acute thrombotic event. Identification of plaques that are vulnerable to rupture is paramount to the prevention of heart attacks and strokes, but a noninvasive plaque characterization imaging technology that is cost-effective, safe, and accurate has remained elusive. The goal of this dissertation is to evaluate whether acoustic radiation force impulse (ARFI) imaging, an ultrasound-based elastography technique, can noninvasively characterize plaque components and identify features that have been shown to correlate with plaque vulnerability. Data are presented from preclinical studies, done in a porcine model of atherosclerosis, and clinical studies, performed in patients undergoing carotid endarterectomy (CEA), to demonstrate the sensitivity and specificity of ARFI for various plaque components. Additionally, the ability of ARFI to measure fibrous cap thickness is assessed with finite element method (FEM) modelling, and the limits of ARFI fibrous cap resolution are analyzed. Lastly, advanced ARFI-based plaque imaging methods are explored, including intravascular ARFI for coronary plaque characterization. Overall, these studies demonstrate that ARFI can delineate features consistent with vulnerable plaque in a clinical imaging context and suggest that ARFI has the potential to improve the current state of the art in atherosclerosis diagnostics.Doctor of Philosoph