Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. If the object possesses spatially variant acoustic properties that are unaccounted for by the reconstruction method, the estimated image can contain distortions. While reconstruction methods have recently been developed to compensate for this effect, they generally require the object's acoustic properties to be known a priori. To circumvent the need for detailed information regarding an object's acoustic properties, we previously proposed a half-time reconstruction method for PACT. A half-time reconstruction method estimates the PACT image from a data set that has been temporally truncated to exclude the data components that have been strongly aberrated. However, this method can be improved upon when the approximate sizes and locations of isolated heterogeneous structures, such as bones or gas pockets, are known. To address this, we investigate PACT reconstruction methods that are based on a variable data truncation (VDT) approach. The VDT approach represents a generalization of the half-time approach, in which the degree of temporal truncation for each measurement is determined by the distance between the corresponding ultrasonic transducer location and the nearest known bone or gas void location. Computer-simulated and experimental data are employed to demonstrate the effectiveness of the approach in mitigating artifacts due to acoustic heterogeneities

AI-powered transmitted light microscopy for functional analysis of live cells

Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling

Applying Mechanistic Understanding of Optical Absorption and Scattering Phenomena to Enhance the Spectroscopic Analyses of Pharmaceutical Materials

The dissertation uses spatially-resolved spectroscopy to separate absorption and scattering behaviors when NIR light interacts with pharmaceutical materials. The separated absorption and scattering were utilized to enhance mechanistic understanding of NIR diffuse reflectance spectroscopy and improve practical spectroscopic analysis in pharmaceutical applications. Near-Infrared (NIR) chemical imaging based spatially-resolved spectroscopy was used to measure radially-diffused reflectance on pharmaceutical materials. A Monte Carlo simulation based partial least square (PLS) model was constructed to determine the absorption and reduced scattering coefficients in pharmaceutical samples from the measured radially-diffused reflectance. The separated absorption and reduced scattering coefficients were combined with Monte Carlo simulation to provide understanding of the effects of physical properties (e.g., particle size and tablet density) on NIR spectral responses, including absorption and depth of penetration profiles. It was discovered that absorption and reduced scattering coefficients are the dominant factors in determining NIR absorbance and depth of penetration profiles, respectively. Both empirical measurements and Monte Carlo simulation were used to explore the photon radial movements in a chemical imaging setting. It is well understood that radial photon movements among adjacent pixels leaded to blurred 2-D chemical images. A Monte Carlo simulation based deconvolution filter was developed to sharpen a blurred feature in a 2-D image while maintaining the original chemical content of that feature. A new scattering correction method via the reduced scattering coefficient was proposed to specifically reduce physical interference with predictions of chemical properties. The wavelength- and absorption- dependent properties of the reduced scattering coefficient were found to allow specific suppression of physical interference and maintain the original chemical information. Combing the separated optical coefficients with contemporary efficient calibration approaches was found to simplify multivariate model calibration using a reduced calibration dataset, allowing parsimonious multivariate models, and reaching the same or even lower prediction error. To the author\u27s best knowledge, this work is the first example of the application of spatially-resolved spectroscopy to the pharmaceutical field. The enhanced understanding and improved spectroscopic analysis demonstrated in this dissertation is expected to provide groundwork for a wide variety of applications of spatially-resolved spectroscopy in pharmaceutical analyses

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Fast widefield techniques for fluorescence and phase endomicroscopy

Thesis (Ph.D.)--Boston UniversityEndomicroscopy is a recent development in biomedical optics which gives researchers and physicians microscope-resolution views of intact tissue to complement macroscopic visualization during endoscopy screening. This thesis presents HiLo endomicroscopy and oblique back-illumination endomicroscopy, fast widefield imaging techniques with fluorescence and phase contrast, respectively. Fluorescence imaging in thick tissue is often hampered by strong out-of-focus background signal. Laser scanning confocal endomicroscopy has been developed for optically-sectioned imaging free from background, but reliance on mechanical scanning fundamentally limits the frame rate and represents significant complexity and expense. HiLo is a fast, simple, widefield fluorescence imaging technique which rejects out-of-focus background signal without the need for scanning. It works by acquiring two images of the sample under uniform and structured illumination and synthesizing an optically sectioned result with real-time image processing. Oblique back-illumination microscopy (OBM) is a label-free technique which allows, for the first time, phase gradient imaging of sub-surface morphology in thick scattering tissue with a reflection geometry. OBM works by back-illuminating the sample with the oblique diffuse reflectance from light delivered via off-axis optical fibers. The use of two diametrically opposed illumination fibers allows simultaneous and independent measurement of phase gradients and absorption contrast. Video-rate single-exposure operation using wavelength multiplexing is demonstrated

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Data driven approaches for investigating molecular heterogeneity of the brain

It has been proposed that one of the clearest organizing principles for most sensory systems is the existence of parallel subcircuits and processing streams that form orderly and systematic mappings from stimulus space to neurons. Although the spatial heterogeneity of the early olfactory circuitry has long been recognized, we know comparatively little about the circuits that propagate sensory signals downstream. Investigating the potential modularity of the bulb’s intrinsic circuits proves to be a difficult task as termination patterns of converging projections, as with the bulb’s inputs, are not feasibly realized. Thus, if such circuit motifs exist, their detection essentially relies on identifying differential gene expression, or “molecular signatures,” that may demarcate functional subregions. With the arrival of comprehensive (whole genome, cellular resolution) datasets in biology and neuroscience, it is now possible for us to carry out large-scale investigations and make particular use of the densely catalogued, whole genome expression maps of the Allen Brain Atlas to carry out systematic investigations of the molecular topography of the olfactory bulb’s intrinsic circuits. To address the challenges associated with high-throughput and high-dimensional datasets, a deep learning approach will form the backbone of our informatic pipeline. In the proposed work, we test the hypothesis that the bulb’s intrinsic circuits are parceled into distinct, parallel modules that can be defined by genome-wide patterns of expression. In pursuit of this aim, our deep learning framework will facilitate the group-registration of the mitral cell layers of ~ 50,000 in-situ olfactory bulb circuits to test this hypothesis

Application of artificial vision algorithms to images of microscopy and spectroscopy for the improvement of cancer diagnosis

El diagnóstico final de la mayoría de tipos de cáncer lo realiza un médico experto en anatomía patológica que examina muestras tisulares o celulares sospechosas extraídas del paciente. Actualmente, esta evaluación depende en gran medida de la experiencia del médico y se lleva a cabo de forma cualitativa mediante técnicas de imagen tradicionales como la microscopía óptica. Esta tarea tediosa está sujeta a altos grados de subjetividad y da lugar a niveles de discordancia inadecuados entre diferentes patólogos, especialmente en las primeras etapas de desarrollo del cáncer. La espectroscopía infrarroja por Transformada de Fourier (siglas FTIR en inglés) es una tecnología ampliamente utilizada en la industria que recientemente ha demostrado una capacidad creciente para mejorar el diagnóstico de diferentes tipos de cáncer. Esta técnica aprovecha las propiedades del infrarrojo medio para excitar los modos vibratorios de los enlaces químicos que forman las muestras biológicas. La principal señal generada consiste en un espectro de absorción que informa sobre la composición química de la muestra iluminada. Los microespectrómetros FTIR modernos, compuestos por complejos componentes ópticos y detectores matriciales de alta sensibilidad, permiten capturar en un laboratorio de investigación común imágenes hiperespectrales de alta calidad que aúnan información química y espacial. Las imágenes FTIR son estructuras de datos ricas en información que se pueden analizar individualmente o junto con otras modalidades de imagen para realizar diagnósticos patológicos objetivos. Por lo tanto, esta técnica de imagen emergente alberga un alto potencial para mejorar la detección y la graduación del riesgo del paciente en el cribado y vigilancia de cáncer. Esta tesis estudia e implementa diferentes metodologías y algoritmos de los campos interrelacionados de procesamiento de imagen, visión por ordenador, aprendizaje automático, reconocimiento de patrones, análisis multivariante y quimiometría para el procesamiento y análisis de imágenes hiperespectrales FTIR. Estas imágenes se capturaron con un moderno microscopio FTIR de laboratorio a partir de muestras de tejidos y células afectadas por cáncer colorrectal y de piel, las cuales se prepararon siguiendo protocolos alineados con la práctica clínica actual. Los conceptos más relevantes de la espectroscopía FTIR se investigan profundamente, ya que deben ser comprendidos y tenidos en cuenta para llevar a cabo una correcta interpretación y tratamiento de sus señales especiales. En particular, se revisan y analizan diferentes factores fisicoquímicos que influyen en las mediciones espectroscópicas en el caso particular de muestras biológicas y pueden afectar críticamente su análisis posterior. Todos estos conceptos y estudios preliminares entran en juego en dos aplicaciones principales. La primera aplicación aborda el problema del registro o alineación de imágenes hiperespectrales FTIR con imágenes en color adquiridas con microscopios tradicionales. El objetivo es fusionar la información espacial de distintas muestras de tejido medidas con esas dos modalidades de imagen y centrar la discriminación en las regiones seleccionadas por los patólogos, las cuales se consideran más relevantes para el diagnóstico de cáncer colorrectal. En la segunda aplicación, la espectroscopía FTIR se lleva a sus límites de detección para el estudio de las entidades biomédicas más pequeñas. El objetivo es evaluar las capacidades de las señales FTIR para discriminar de manera fiable diferentes tipos de células de piel que contienen fenotipos malignos. Los estudios desarrollados contribuyen a la mejora de métodos de decisión objetivos que ayuden al patólogo en el diagnóstico final del cáncer. Además, revelan las limitaciones de los protocolos actuales y los problemas intrínsecos de la tecnología FTIR moderna, que deberían abordarse para permitThe final diagnosis of most types of cancers is performed by an expert clinician in anatomical pathology who examines suspicious tissue or cell samples extracted from the patient. Currently, this assessment largely relies on the experience of the clinician and is accomplished in a qualitative manner by means of traditional imaging techniques, such as optical microscopy. This tedious task is subject to high degrees of subjectivity and gives rise to suboptimal levels of discordance between different pathologists, especially in early stages of cancer development. Fourier Transform infrared (FTIR) spectroscopy is a technology widely used in industry that has recently shown an increasing capability to improve the diagnosis of different types of cancer. This technique takes advantage of the ability of mid-infrared light to excite the vibrational modes of the chemical bonds that form the biological samples. The main generated signal consists of an absorption spectrum that informs of the chemical composition of the illuminated specimen. Modern FTIR microspectrometers, composed of complex optical components and high-sensitive array detectors, allow the acquisition of high-quality hyperspectral images with spatially-resolved chemical information in a common research laboratory. FTIR images are information-rich data structures that can be analysed alone or together with other imaging modalities to provide objective pathological diagnoses. Hence, this emerging imaging technique presents a high potential to improve the detection and risk stratification in cancer screening and surveillance. This thesis studies and implements different methodologies and algorithms from the related fields of image processing, computer vision, machine learning, pattern recognition, multivariate analysis and chemometrics for the processing and analysis of FTIR hyperspectral images. Those images were acquired with a modern benchtop FTIR microspectrometer from tissue and cell samples affected by colorectal and skin cancer, which were prepared by following protocols close to the current clinical practise. The most relevant concepts of FTIR spectroscopy are thoroughly investigated, which ought to be understood and considered to perform a correct interpretation and treatment of its special signals. In particular, different physicochemical factors are reviewed and analysed, which influence the spectroscopic measurements for the particular case of biological samples and can critically affect their later analysis. All these knowledge and preliminary studies come into play in two main applications. The first application tackles the problem of registration or alignment of FTIR hyperspectral images with colour images acquired with traditional microscopes. The aim is to fuse the spatial information of distinct tissue samples measured by those two imaging modalities and focus the discrimination on regions selected by the pathologists, which are meant to be the most relevant areas for the diagnosis of colorectal cancer. In the second application, FTIR spectroscopy is pushed to their limits of detection for the study of the smallest biomedical entities. The aim is to assess the capabilities of FTIR signals to reliably discriminate different types of skin cells containing malignant phenotypes. The developed studies contribute to the improvement of objective decision methods to support the pathologist in the final diagnosis of cancer. In addition, they reveal the limitations of current protocols and intrinsic problems of modern FTIR technology, which should be tackled in order to enable its transference to anatomical pathology laboratories in the future.El diagnòstic final de la majoria de tipus de càncer ho realitza un metge expert en anatomia patològica que examina mostres tissulars o cel¿lulars sospitoses extretes del pacient. Actualment, aquesta avaluació depèn en gran part de l'experiència del metge i es porta a terme de forma qualitativa mitjançant tècniques d'imatge tradicionals com la microscòpia òptica. Aquesta tasca tediosa està subjecta a alts graus de subjectivitat i dóna lloc a nivells de discordança inadequats entre diferents patòlegs, especialment en les primeres etapes de desenvolupament del càncer. L'espectroscòpia infraroja per Transformada de Fourier (sigles FTIR en anglès) és una tecnologia àmpliament utilitzada en la indústria que recentment ha demostrat una capacitat creixent per millorar el diagnòstic de diferents tipus de càncer. Aquesta tècnica aprofita les propietats de l'infraroig mitjà per excitar els modes vibratoris dels enllaços químics que formen les mostres biològiques. El principal senyal generat consisteix en un espectre d'absorció que informa sobre la composició química de la mostra il¿luminada. Els microespectrómetres FTIR moderns, compostos per complexos components òptics i detectors matricials d'alta sensibilitat, permeten capturar en un laboratori d'investigació comú imatges hiperespectrals d'alta qualitat que uneixen informació química i espacial. Les imatges FTIR són estructures de dades riques en informació que es poden analitzar individualment o juntament amb altres modalitats d'imatge per a realitzar diagnòstics patològics objectius. Per tant, aquesta tècnica d'imatge emergent té un alt potencial per a millorar la detecció i la graduació del risc del pacient en el cribratge i vigilància de càncer. Aquesta tesi estudia i implementa diferents metodologies i algoritmes dels camps interrelacionats de processament d'imatge, visió per ordinador, aprenentatge automàtic, reconeixement de patrons, anàlisi multivariant i quimiometria per al processament i anàlisi d'imatges hiperespectrals FTIR. Aquestes imatges es van capturar amb un modern microscopi FTIR de laboratori a partir de mostres de teixits i cèl¿lules afectades per càncer colorectal i de pell, les quals es van preparar seguint protocols alineats amb la pràctica clínica actual. Els conceptes més rellevants de l'espectroscòpia FTIR s'investiguen profundament, ja que han de ser compresos i tinguts en compte per dur a terme una correcta interpretació i tractament dels seus senyals especials. En particular, es revisen i analitzen diferents factors fisicoquímics que influeixen en els mesuraments espectroscòpiques en el cas particular de mostres biològiques i poden afectar críticament la seua anàlisi posterior. Tots aquests conceptes i estudis preliminars entren en joc en dues aplicacions principals. La primera aplicació aborda el problema del registre o alineació d'imatges hiperespectrals FTIR amb imatges en color adquirides amb microscopis tradicionals. L'objectiu és fusionar la informació espacial de diferents mostres de teixit mesurades amb aquestes dues modalitats d'imatge i centrar la discriminació en les regions seleccionades pels patòlegs, les quals es consideren més rellevants per al diagnòstic de càncer colorectal. En la segona aplicació, l'espectroscòpia FTIR es porta als seus límits de detecció per a l'estudi de les entitats biomèdiques més xicotetes. L'objectiu és avaluar les capacitats dels senyals FTIR per discriminar de manera fiable diferents tipus de cèl¿lules de pell que contenen fenotips malignes. Els estudis desenvolupats contribueixen a la millora de mètodes de decisió objectius que ajuden el patòleg en el diagnòstic final del càncer. A més, revelen les limitacions dels protocols actuals i els problemes intrínsecs de la tecnologia FTIR moderna, que haurien d'abordar per permetre la seva transferència als laboratoris d'anatomia patològica en el futur.Peñaranda Gómez, FJ. (2018). Application of artificial vision algorithms to images of microscopy and spectroscopy for the improvement of cancer diagnosis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99748TESI