Biomedical Photoacoustic Imaging and Sensing Using Affordable Resources

The overarching goal of this book is to provide a current picture of the latest developments in the capabilities of biomedical photoacoustic imaging and sensing in an affordable setting, such as advances in the technology involving light sources, and delivery, acoustic detection, and image reconstruction and processing algorithms. This book includes 14 chapters from globally prominent researchers , covering a comprehensive spectrum of photoacoustic imaging topics from technology developments and novel imaging methods to preclinical and clinical studies, predominantly in a cost-effective setting. Affordability is undoubtedly an important factor to be considered in the following years to help translate photoacoustic imaging to clinics around the globe. This first-ever book focused on biomedical photoacoustic imaging and sensing using affordable resources is thus timely, especially considering the fact that this technique is facing an exciting transition from benchtop to bedside. Given its scope, the book will appeal to scientists and engineers in academia and industry, as well as medical experts interested in the clinical applications of photoacoustic imaging

Data-driven quantitative photoacoustic tomography

Spatial information about the 3D distribution of blood oxygen saturation (sO2) in vivo is of clinical interest as it encodes important physiological information about tissue health/pathology. Photoacoustic tomography (PAT) is a biomedical imaging modality that, in principle, can be used to acquire this information. Images are formed by illuminating the sample with a laser pulse where, after multiple scattering events, the optical energy is absorbed. A subsequent rise in temperature induces an increase in pressure (the photoacoustic initial pressure p0) that propagates to the sample surface as an acoustic wave. These acoustic waves are detected as pressure time series by sensor arrays and used to reconstruct images of sample’s p0 distribution. This encodes information about the sample’s absorption distribution, and can be used to estimate sO2. However, an ill-posed nonlinear inverse problem stands in the way of acquiring estimates in vivo. Current approaches to solving this problem fall short of being widely and successfully applied to in vivo tissues due to their reliance on simplifying assumptions about the tissue, prior knowledge of its optical properties, or the formulation of a forward model accurately describing image acquisition with a specific imaging system. Here, we investigate the use of data-driven approaches (deep convolutional networks) to solve this problem. Networks only require a dataset of examples to learn a mapping from PAT data to images of the sO2 distribution. We show the results of training a 3D convolutional network to estimate the 3D sO2 distribution within model tissues from 3D multiwavelength simulated images. However, acquiring a realistic training set to enable successful in vivo application is non-trivial given the challenges associated with estimating ground truth sO2 distributions and the current limitations of simulating training data. We suggest/test several methods to 1) acquire more realistic training data or 2) improve network performance in the absence of adequate quantities of realistic training data. For 1) we describe how training data may be acquired from an organ perfusion system and outline a possible design. Separately, we describe how training data may be generated synthetically using a variant of generative adversarial networks called ambientGANs. For 2), we show how the accuracy of networks trained with limited training data can be improved with self-training. We also demonstrate how the domain gap between training and test sets can be minimised with unsupervised domain adaption to improve quantification accuracy. Overall, this thesis clarifies the advantages of data-driven approaches, and suggests concrete steps towards overcoming the challenges with in vivo application

Learning Tissue Geometries for Photoacoustic Image Analysis

Photoacoustic imaging (PAI) holds great promise as a novel, non-ionizing imaging modality, allowing insight into both morphological and physiological tissue properties, which are of particular importance in the diagnostics and therapy of various diseases, such as cancer and cardiovascular diseases. However, the estimation of physiological tissue properties with PAI requires the solution of two inverse problems, one of which, in particular, presents challenges in the form of inherent high dimensionality, potential ill-posedness, and non-linearity. Deep learning (DL) approaches show great potential to address these challenges but typically rely on simulated training data providing ground truth labels, as there are no gold standard methods to infer physiological properties in vivo. The current domain gap between simulated and real photoacoustic (PA) images results in poor in vivo performance and a lack of reliability of models trained with simulated data. Consequently, the estimates of these models occasionally fail to match clinical expectations. The work conducted within the scope of this thesis aimed to improve the applicability of DL approaches to PAI-based tissue parameter estimation by systematically exploring novel data-driven methods to enhance the realism of PA simulations (learning-to-simulate). This thesis is part of a larger research effort, where different factors contributing to PA image formation are disentangled and individually approached with data-driven methods. The specific research focus was placed on generating tissue geometries covering a variety of different tissue types and morphologies, which represent a key component in most PA simulation approaches. Based on in vivo PA measurements (N = 288) obtained in a healthy volunteer study, three data-driven methods were investigated leveraging (1) semantic segmentation, (2) Generative Adversarial Networks (GANs), and (3) scene graphs that encode prior knowledge about the general tissue composition of an image, respectively. The feasibility of all three approaches was successfully demonstrated. First, as a basis for the more advanced approaches, it was shown that tissue geometries can be automatically extracted from PA images through the use of semantic segmentation with two types of discriminative networks and supervised training with manual reference annotations. While this method may replace manual annotation in the future, it does not allow the generation of any number of tissue geometries. In contrast, the GAN-based approach constitutes a generative model that allows the generation of new tissue geometries that closely follow the training data distribution. The plausibility of the generated geometries was successfully demonstrated in a comparative assessment of the performance of a downstream quantification task. A generative model based on scene graphs was developed to gain a deeper understanding of important underlying geometric quantities. Unlike the GAN-based approach, it incorporates prior knowledge about the hierarchical composition of the modeled scene. However, it allowed the generation of plausible tissue geometries and, in parallel, the explicit matching of the distributions of the generated and the target geometric quantities. The training was performed either in analogy to the GAN approach, with target reference annotations, or directly with target PA images, circumventing the need for annotations. While this approach has so far been exclusively conducted in silico, its inherent versatility presents a compelling prospect for the generation of tissue geometries with in vivo reference PA images. In summary, each of the three approaches for generating tissue geometry exhibits distinct strengths and limitations, making their suitability contingent upon the specific application at hand. By opening a new research direction in the form of learning-to-simulate approaches and significantly improving the realistic modeling of tissue geometries and, thus, ultimately, PA simulations, this work lays a crucial foundation for the future use of DL-based quantitative PAI in the clinical setting

Developing opto-acoustic microscopy instruments for biomedical imaging applications

Opto-acoustic Microscopy is an emerging technique for cross-sectional imaging that provides structural and functional volumetric information with micrometer resolution. This allows for non-invasive detection of endogenous contrast agents and chromophores without using ionizing radiation. The objective of this thesis is to investigate the potential of opto-acoustic microscopy combined with optical coherence tomography in developing advanced diagnostic tools for biomedical applications, in particular for cancer diagnosis. To achieve this objective, the thesis focuses on in-vivo multi-spectral optoacoustic microscopy imaging of multiple endogenous contrast agents in Xenopus laevis. The study used a high-resolution opto-acoustic microscopy instrument capable of multi-pectral imaging covering two octaves of the spectrum, and a novel technique to distinguish between different chromophores in the sample. An optical coherence tomography instrument was integrated in the opto-acoustic microscopy system to guide imaging and provide reliable structural information. Additionally, visible light optical coherence tomography system was developed as an ultra-high resolution alternative. Prior to this study, mapping of lipids in Xenopus laevis was achieved using an in-house all-fibre supercontinuum optical source developed in DTU, Denmark operating in the extended near-infrared region. Both opto-acoustic microscopy and optical coherence tomography instruments are capable of acquiring cross-sectional and volumetric images in real-time. Finally, a high-resolution opto-acoustic microscopy set-up to explore the impact of picosecond pulse duration excitation on the axial resolution of the imaging system. The study compared a picosecond pulse duration laser-based optoacoustic microscopy instrument to a nanosecond laser-based one in terms of axial resolution and obtained unprecedented in-vivo images of the brain in Xenopus laevis tadpoles

Laser doppler vibrometry for cardiovascular monitoring and photoacoustic imaging

Nowadays, techniques for health monitoring mainly require physical contact with patients, which is not always ideal. Non-contact health monitoring has become an important research topic in the last decades. The non-contact detection of a patient's health condition represents a beneficial tool in different biomedical fields. Examples can be found in intensive care, home health care, the nursing of the elderly, the monitoring of physical efforts, and in human-machine interactions. Cardiovascular diseases (CV) are one of the most spread causes of death in developed countries. Their monitoring techniques involve physical contact with patients. A non-contact technique for cardiovascular monitoring could overcome problems related to the contact with the patient such as skin lesions. It could also expand the availability of monitoring to those cases where contact is not possible or should be avoided to reduce the exposure of medical personnel to biochemical hazard conditions.Several research groups have investigated different techniques for non-contact monitoring of health; among them, the laser Doppler Vibrometry (LDVy) has one of the highest accuracies and signal to noise ratios for cardiorespiratory signals detection. Moreover, the simplicity of data processing, the long-distance measurement range, and the high bandwidth make the laser Doppler vibrometer (LDV) suitable for daily measurements. LDVy is an interferometric technique employed for the measurements of displacement or velocity signals in various fields. In particular, it is deployed in the biomedical field for the extraction of several cardiovascular parameters, such as the PR-time. Generally, the extraction of these parameters requires ideal measuring conditions (measuring spot and laser direction), which are not realistic for daily monitoring in non-laboratory conditions, and especially in tracking applications. The first scientific hypothesis of this work is that the PR-time detected with LDV has an acceptable uncertainty for a realistic variety of measurement spot positions and angles of the incident laser beam. Therefore, I investigated the uncertainty contribution to the detection of the PR-time from LDV signals resulting from the laser beam direction and from the measurement point position; these investigations were carried out with a multipoint laser Doppler vibrometer. The uncertainties were evaluated according to the Guide to the Expression of Uncertainty in Measurement. Successively, the ranges of PR-time values where it is possible to state with 95% certainty that a diagnosis is correct are identified. Normal values of PR-time are included in the range 120 ms -200 ms. For single value measurements with precise alignment the reliable range for the detection of the healthy condition is 146.4 ms -173.6 ms. The detection of CV diseases is reliable for measured values lower than 93.6 ms and greater than 226.4 ms. For mean value measurements with precise alignment the reliable range for the detection of the healthy condition is 126.6 ms -193.4 ms. The detection of CV diseases is reliable for measured values lower than 113.4 ms and greater than 206.6 ms. Therefore, for measured values included in the mentioned ranges, the detection of the PR-time and relative diagnosis with the LDVy in non-laboratory conditions is reliable. The method for the estimation of the uncertainty contribution proposed in this work can be applied to other cardiovascular parameters extracted with the LDVy. Recently, the LDVy was employed for the detection of tumors in tissue-mimic phantoms as a noncontact alternative to the ultrasound sensors employed in photoacoustic imaging (PAI). A non-contact method has considerable advantages for photoacustic imaging, too. Several works present the possibility to perform PAI measurements with LDVy. However, a successful detection of the signals generated by a tumor depends on the metrological characteristics of the LDV, on the properties of the tumor and of the tissue. The conditions under which a tumor is detectable with the laser Doppler vibrometer has not been investigated yet. The second scientific hypothesis of this work is that, under certain conditions, photoacoustic imaging measurements with LDVy are feasible. Therefore, I identified those conditions to determine the detection limits of LDVy for PAI measurements. These limits were deduced by considering the metrological characteristics of a commercial LDV, the dimensions and the position of the tumor in the tissue. I derived a model for the generation and propagation of PA signals and its detection with an LDV. The model was validated by performing experiments on silicone tissue-micking phantoms. The validated model with breast-tissue parameters reveals the limits of tumor detection with LDVy-based PAI. The results show that commercial LDVs can detect tumors with a minimal radius of ≈350 μm reliably if they are located at a maximal depth in tissue of ≈2 cm. Depending on the position of the detection point, the maximal depth can diminish and depending on the absorption characteristics of the tumor, the detection range increases.Heutzutage erfordern Techniken zur Gesundheitsüberwachung hauptsächlich den physischen Kontakt mit dem Patienten, was nicht immer ideal ist. Die berührungslose Gesundheitsüberwachung hat sich in den letzten Jahrzehnten zu einem wichtigen Forschungsthema entwickelt. Die berührungslose Erkennung des Gesundheitszustands eines Patienten stellt ein nützliches Instrument in verschiedenen biomedizinischen Bereichen dar. Beispiele finden sich in der Intensivpflege, der häuslichen Krankenpflege, der Altenpflege, der Überwachung körperlicher Anstrengungen und in der MenschMaschine-Interaktion. Herz-Kreislauf-Erkrankungen sind eine der am weitesten verbreiteten Todesursachen in den Industrieländern. Ihre Überwachungstechniken erfordern einen physischen Kontakt mit den Patienten. Eine berührungslose Technik für die Überwachung von Herz-KreislaufErkrankungen könnte Probleme im Zusammenhang mit dem Kontakt mit dem Patienten, wie z. B. Hautverletzungen, überwinden. Verschiedene Messgeräte wurden für die berührungslose Überwachung der Gesundheit untersucht; unter ihnen hat das Laser-Doppler-Vibrometrer (LDV) eine der höchsten Genauigkeiten und Signal-Rausch-Verhältnisse für die Erkennung kardiorespiratorischer Signale. Darüber hinaus ist das Laser-Doppler-Vibrometer (LDV) aufgrund der einfachen Datenverarbeitung, des großen Messbereichs und der hohen Bandbreite für tägliche Messungen geeignet. LDV ist ein interferometrisches Verfahren, das zur Messung von Weg- oder Geschwindigkeitssignalen in verschiedenen Bereichen eingesetzt wird. Insbesondere wird es im biomedizinischen Bereich für die Extraktion verschiedener kardiovaskulärer Parameter, wie z. B. der PR-Zeit, eingesetzt. Im Allgemeinen erfordert die Extraktion dieser Parameter ideale Messbedingungen (Messfleck und Laserrichtung), die für die tägliche Überwachung unter Nicht-Laborbedingungen und insbesondere für TrackingAnwendungen nicht realistisch sind. Die erste wissenschaftliche Hypothese dieser Arbeit ist, dass die mit dem LDV ermittelte PR-Zeit eine akzeptable Unsicherheit für eine realistische Vielzahl von Messpunktpositionen und Winkeln des einfallenden Laserstrahls aufweist. Daher wurde der Unsicherheitsbeitrag zur Ermittlung der PR-Zeit aus LDV-Signalen untersucht, der sich aus der Laserstrahlrichtung und der Messpunktposition ergibt; diese Untersuchungen wurden mit einem Mehrpunkt-Laser-Doppler-Vibrometer durchgeführt. Die Unsicherheiten wurden gemäß der Technische Regel ISO/IEC Guide 98-3:2008-09 Messunsicherheit – Teil 3: Leitfaden zur Angabe der Unsicherheit beim Messen bewertet. Nacheinander werden die Bereiche der PR-Zeit-Werte ermittelt, in denen mit 95%iger Sicherheit eine korrekte Diagnose gestellt werden kann. Die in dieser Arbeit vorgeschlagene Methode zur Schätzung des Unsicherheitsbeitrags kann auch auf andere kardiovaskuläre Parameter angewendet werden, die mit dem LDV extrahiert werden. Kürzlich wurde das LDV zur Erkennung von Tumoren in gewebeähnlichen Phantomen als berührungslose Alternative zu den Ultraschallsensoren eingesetzt, die bei der photoakustischen Bildgebung (PAI) verwendet werden. Eine berührungslose Methode hat auch für die photoakustische Bildgebung erhebliche Vorteile. In mehreren Arbeiten wird die Möglichkeit vorgestellt, PAIMessungen mit LDV durchzuführen. Die erfolgreiche Erkennung der von einem Tumor erzeugten Signale hängt jedoch von den messtechnischen Eigenschaften des LDV sowie von den Eigenschaften des Tumors und des Gewebes ab. Die Bedingungen, unter denen ein Tumor mit dem LDV detektierbar ist, wurden bisher nicht untersucht. Die zweite wissenschaftliche Hypothese dieser Arbeit ist, dass unter bestimmten Bedingungen photoakustische Bildgebungsmessungen mit dem LDV möglich sind. Daher wurden diese Bedingungen ermittelt, um die Nachweisgrenzen von LDV für PAI-Messungen zu bestimmen. Diese Grenzen wurden unter Berücksichtigung der messtechnischen Eigenschaften eines handelsüblichen LDV, der Abmessungen und der Position des Tumors im Gewebe abgeleitet. In dieser Arbeit wurde ein Modell für die Erzeugung und Ausbreitung von PA-Signalen und deren Nachweis mit einem LDV abgeleitet. Das Modell wurde durch Experimente an Silikongewebe-Phantomen validiert. Das validierte Modell mit Parametern des Brustgewebes zeigt die Grenzen der Tumorerkennung mit LDV-basierter PAI auf. Die Ergebnisse zeigen, dass kommerzielle LDV Tumore mit einem minimalen Radius von ≈350 μm zuverlässig erkennen können

Nanoparticle-Enabled In Vivo Photoacoustic Molecular Imaging of Cancer

Photoacoustic (PA) imaging is an emerging biomedical imaging modality that combines optical and ultrasound imaging technologies. PA imaging relies on the absorption of electromagnetic energy (usually in the form of visible or near-infrared light) leading to the generation of acoustic waves by thermoelastic expansion, which can be detected with an ultrasound detector. PA imaging can be used to detect endogenous chromophores such as deoxyhemoglobin and oxyhemoglobin, or can be used together with external nanosensors for added functionality. The former is used to measure things like blood oxygenation, while the latter opens up many possibilities for PA imaging, limited only to the availability of optical nanosensors. In this dissertation, I employ the use of PA nanosensors for contrast enhancement and molecular imaging in in vivo small animal cancer models. In the first section, I introduce a novel PA background reduction technique called the transient triplet differential (TTD) method. The TTD method exploits the fact that phosphorescent dyes possess a triplet state with a unique red-shifted absorption wavelength, distinct from its ordinary singlet state absorption profile. By pumping these dyes into the triplet state and comparing the signal to the unpumped dyes, a differential signal can be obtained which solely originates from these dyes. Since intrinsic chromophores of biological tissue are not able to undergo intersystem crossing and enter the triplet state, the TTD method can facilitate “true” background free molecular imaging by excluding the signals from every other chromophore outside the phosphorescent dye. Here, I demonstrate up to an order of magnitude better sensitivity of the TTD method compared to other existing contrast enhancement techniques in both in vitro experiments and in vivo cancer models. In the second section, I explore the use of a nanoparticle formulation of a repurposed FDA-approved drug called clofazimine for diagnosis of prostate cancer. Clofazimine nanoparticles have a high optical absorbance at 495 nm and has been known to specifically accumulate in macrophages as they form stable crystal-like inclusions once they are uptaken by macrophages. Due to the presence of tumor associated macrophages, it is expected that clofazimine would accumulate in much higher quantities in the cancerous prostate compared to normal prostates. Here, I show that there was indeed a significantly higher accumulation of clofazimine nanoparticles in cancerous prostates compared to normal prostates in a transgenic mouse model, which was detectable both using histology and ex vivo PA imaging. In the third and final section, I explore the use of a potassium (K+) nanosensor together with PA imaging in measuring the in vivo K+ distribution in the tumor microenvironment (TME). K+ is the most abundant ion in the body and has recently been shown to be at a significantly higher concentration in the tumor. The reported 5-10 fold elevation (25-50 mM compared to 5mM) in the tumor has been shown to inhibit immune cell efficacy, and thus immunotherapy. Despite the abundance and importance of K+ in the body, few ways exist to measure it in vivo. In this study, a solvatochromic dye K+ nanoparticle (SDKNP) was used together with PA imaging to quantitatively measure the in vivo distribution of K+ in the TME. Significantly elevated K+ levels were found in the TME, with an average concentration of approximately 29 mM, matching the values found by the previous study. The results were then verified using mass spectrometry.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155291/1/tanjoel_1.pd

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Supercontinuum sources in the practice of multimodal imaging

The development of recent imaging modalities and of multimodal imaging may offer new perspectives for biomedical imaging, such as in-vivo cancer detection at early stages. By combining optical coherence tomography (OCT) and photoacoustic microscopy (PAM), complementary information is extracted from tissue: scattering and absorption. Non-invasive cross-sectional images with micrometre resolution are obtained. In this thesis, for the first time, encouraging results using a single SC source for OCT and PAM are obtained. Micrometre axial resolution is achieved using SC sources for OCT. The use of SC sources for PAM allows for multispectral PAM (MPAM) by using several excitation spectral bands. With MPAM, different absorbers are distinguishable and recognisable through their absorption spectra. In addition, for the first time, spectroscopic photoacoustic (sPA) measurements are demonstrated in the visible using a bandwidth narrower than 40 nm. These results were obtained with the first multimodal imaging system that combines sPA, PAM, MPAM and OCT. A single commercially available SC source is used for excitation. Diverse in-vitro and in-vivo samples are imaged to show the capabilities of such a configuration. In addition, the development of a novel fibre-based SC source with both increased energy density and pulse repetition frequency (PRF) is presented. The increased pulse energy allows reduction of excitations bands that leads to more accurate MPAM and sPA measurements, while the access to larger PRFs allows for both noise reduction and faster imaging rates in PAM and OCT. A tapered photonic crystal fibre (PCF) is used to generate the SC by nonlinear spectral broadening. The larger input core of the tapered PCF enables enhanced energy density, where more than 50-100 nJ is achieved with less than 30 nm wide bandwidth, over a broad spectrum extending from 500 nm to 1700 nm. Such a source can be used for in-vivo blood oxygen saturation determination, skin and other superficial organs imaging, which is critical to image tumours and diagnose early stage cancers. Such imaging modalities can also be beneficial during surgery and treatment