DUAL-MODALITY (NEUTRON AND X-RAY) IMAGING FOR CHARACTERIZATION OF PARTIALLY SATURATED GRANULAR MATERIALS AND FLOW THROUGH POROUS MEDIA

Problems involving mechanics of partially saturated soil and physics of flow through porous media are complex and largely unresolved based on using continuum approach. Recent advances in radiation based imaging techniques provide unique access to simultaneously observe continuum scale response while probing corresponding microstructure for developing predictive science and engineering tools in place of phenomenological approach used to date. Recent developments with X-ray/Synchrotron and neutron imaging techniques provided tools to visualize the interior of soil specimen at pore/grain level. X-ray and neutron radiation often presents complementary contrast for given condensed matter in the images due to different fundamental interaction mechanisms. While X-rays mainly interact with the electron clouds, neutrons directly interact with the nucleus of an atom. The dual-modal contrasts are well suited for probing the three phases (silica, air and water) of partially saturated sand since neutrons provide high penetration through large sample size and are very sensitive to water and X-rays of high energy can penetrate moderate sample sizes and clearly show the particle and void phases. Both neutron and X-ray imaging techniques are used to study microstructure of partially saturated compacted sand and water flow behavior through sand with different initial structures. Water distribution in compacted sand with different water contents for different grain shapes of sand was visualized with relatively coarse resolution neutron radiographs and tomograms. Dual-modal contrast of partially saturated sand was presented by using high spatial resolution neutron and X-ray imaging. Advanced image registration technique was used to combine the dual modality data for a more complete quantitative analysis. Quantitative analysis such as grain size distribution, pore size distribution, coordination number, and water saturation along the height were obtained from the image data. Predictive simulations were performed to obtain capillary pressure – saturation curves and simulated two fluid phase (water and air) distribution based image data. In-situ water flow experiments were performed to investigate the effect of initial microstructure. Flow patterns for dense and loose states of Ottawa sand specimens were compared. Flow patterns and water distribution of dense Ottawa and Q-ROK sand specimens was visualized with high resolution neutron and X-ray image data

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Augmented Reality and Artificial Intelligence in Image-Guided and Robot-Assisted Interventions

In minimally invasive orthopedic procedures, the surgeon places wires, screws, and surgical implants through the muscles and bony structures under image guidance. These interventions require alignment of the pre- and intra-operative patient data, the intra-operative scanner, surgical instruments, and the patient. Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. State of the art approaches often support the surgeon by using external navigation systems or ill-conditioned image-based registration methods that both have certain drawbacks. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. Consequently, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. This dissertation investigates the applications of AR, artificial intelligence, and robotics in interventional medicine. Our solutions were applied in a broad spectrum of problems for various tasks, namely improving imaging and acquisition, image computing and analytics for registration and image understanding, and enhancing the interventional visualization. The benefits of these approaches were also discovered in robot-assisted interventions. We revealed how exemplary workflows are redefined via AR by taking full advantage of head-mounted displays when entirely co-registered with the imaging systems and the environment at all times. The proposed AR landscape is enabled by co-localizing the users and the imaging devices via the operating room environment and exploiting all involved frustums to move spatial information between different bodies. The system's awareness of the geometric and physical characteristics of X-ray imaging allows the exploration of different human-machine interfaces. We also leveraged the principles governing image formation and combined it with deep learning and RGBD sensing to fuse images and reconstruct interventional data. We hope that our holistic approaches towards improving the interface of surgery and enhancing the usability of interventional imaging, not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications

Tomografia estendida : do básico até o mapeamento de cérebro de camundongos

Orientador: Mateus Borba CardosoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: Esta tese apresentará uma introdução a imagens de raios-x e como adquirir e processar imagens usando linhas de luz síncrotron. Apresentará os desafios matemáticos e técnicos para reconstruir amostras em três dimensões usando a reconstrução de Tomografia Computadorizada, uma técnica conhecida como CT. Esta técnica tem seu campo de visão limitado ao tamanho da câmera e ao tamanho da iluminação. Uma técnica para ampliar esse campo de visão vai ser apresentada e os desafios técnicos envolvidos para que isso aconteça. Um \textit{pipeline} é proposto e todos os algoritmos necessários foram empacotados em um pacote python chamado Tomosaic. A abordagem baseia-se em adquirir tomogramas parciais em posiçoes pré definidas e depois mesclar os dados em um novo conjunto de dados. Duas maneiras possíveis são apresentadas para essa mescla, uma no domínio das projeções e uma no domínio dos sinogramas. Experimentos iniciais serão então usadas para mostrar que o método proposto funciona com computadores normais. A técnica será aplicada mais tarde para pesquisar a anatomia de cérebros de camundongo completos. Um estudo será apresentado de como obter informação em diferentes escalas do cérebro completo do rato utilizando raios-xAbstract: This thesis will present an introduction to x-ray images and how to acquire and thread images using synchrotron beamlines. It will present the mathematical and technical challenges to reconstruct samples in three dimensions using Computed Tomography reconstruction, a technique known as CT. This technique has a field of view bounded to the camera size and the illumination size. A technique to extended this field of view is going to be presented and the technical challenges involved in order for that to happen will be described. A pipeline is proposed and all the necessary algorithms are contained into a python packaged called Tomosaic. The approach relies on acquired partial tomogram data in a defined grid and later merging the data into a new dataset. Two possible ways are presented in order to that: in the projection domain, and in the sinogram domain. Initial experiments will then be used to show that the pipeline works with normal computers. The technique will be later applied to survey the whole anatomy of whole mouse brains. A study will be shown of how to get the complete range of scales of the mouse brain using x-ray tomography at different resolutionsDoutoradoFísicaDoutor em Ciências163304/2013-01247445/2013, 1456912/2014CNPQCAPE

Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

Quantitative MRI correlates of hippocampal and neocortical pathology in intractable temporal lobe epilepsy

Intractable or drug-resistant epilepsy occurs in over 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to investigate histopathological correlates of quantitative relaxometry and DTI from hippocampal and neocortical specimens of intractable TLE patients. To achieve this goal I developed and evaluated a pipeline for histology to in-vivo MRI image registration, which finds dense spatial correspondence between both modalities. This protocol was divided in two steps whereby sparsely sectioned histology from temporal lobe specimens was first registered to the intermediate ex-vivo MRI which is then registered to the in-vivo MRI, completing a pipeline for histology to in-vivo MRI registration. When correlating relaxometry and DTI with neuronal density and morphology in the temporal lobe neocortex, I found T1 to be a predictor of neuronal density in the neocortical GM and demonstrated that employing multi-parametric MRI (combining T1 and FA together) provided a significantly better fit than each parameter alone in predicting density of neurons. This work was the first to relate in-vivo T1 and FA values to the proportion of neurons in GM. When investigating these quantitative multimodal parameters with histological features within the hippocampal subfields, I demonstrated that MD correlates with neuronal density and size, and can act as a marker for neuron integrity within the hippocampus. More importantly, this work was the first to highlight the potential of subfield relaxometry and diffusion parameters (mainly T2 and MD) as well as volumetry in predicting the extent of cell loss per subfield pre-operatively, with a precision so far unachievable. These results suggest that high-resolution quantitative MRI sequences could impact clinical practice for pre-operative evaluation and prediction of surgical outcomes of intractable epilepsy

Augmenting Wiring Diagrams of Neural Circuits with Activity in Larval Drosophila

Neural circuit models explain an animal's behavior as evoked activity of different circuit elements of its nervous system. Synaptic wiring diagrams mapped from structural imaging of nervous systems guide modeling of neural circuits on the basis of connectivity. However, connectivity alone may not sufficiently constrain the set of plausible circuit hypotheses for empirical study. Combining structural imaging of synaptic connectivity with functional information from activity imaging can further constrain these hypotheses to create unequivocal neural circuit models. This thesis develops computational methods and tools to cross-reference structural and activity imaging of explant larval Drosophila central nervous systems at cellular resolution. Augmenting synaptic wiring diagrams with activity maps via these methods relates circuit structure and function at the neuronal level on a per-behavior basis. Neuronal activity of larval central nervous systems expressing pan-neuronal calcium indicators is imaged in a light sheet microscope, which are then structurally imaged with high throughput electron microscopy. Methods and tools are provided for the assembly of these image volumes, spatial registration between imaging modalities, automated detection of relevant tissue and cellular structures in each, extraction of activity time series, and morphological identification of neurons in structural imaging using reference wiring diagrams mapped from other larvae. Using these methods, existing wiring diagrams mapped from a reference first instar larva were identified with neurons in a larva augmented with activity information for a neural circuit involved in peristaltic motor control. This demonstrates the feasibility of the contributed methods to associate the wiring diagrams of arbitrary circuits of interest with activity timeseries across multiple individuals, behaviors, and behavioral bouts. To demonstrate capability to augment wiring diagrams with information besides activity, these methods are also applied to multiple larvae each expressing specific neurotransmitter labels rather than calcium indicators in the light sheet microscopy. This work scaffolds future modeling of circuits underlying behavior that can only be mechanistically understood in the context of multi-modal observation of synaptic connectivity, functional activity and molecular markers. The methods developed also enable comparative connectomics between multiple individuals, which is necessary to study inter-individual variability in circuits and to observe experimental intervention in the development, structure, and function of neural circuits.Howard Hughes Medical Institute Janelia Research Campu