Design of a low-dose, stationary, tomographic Molecular Breast Imaging system using 3D position sensitive CZT detectors

Molecular Breast Imaging (MBI) has been shown to have high sensitivity for lesion detection, particularly in patients with dense breasts where conventional mammography is limited. However, relatively high radiation dose and long imaging time are limiting factors. Most current MBI systems are based on planar imaging. Improved performance can be achieved using tomographic techniques, which normally involve detector motion. Our goal is to develop a low-dose stationary tomographic MBI system with similar or better performance in terms of lesion detection compared to planar MBI. The proposed system utilizes two opposing CZT detectors with high intrinsic resolution and depth of interaction (DOI) capability, combined with densely packed multi-pinhole collimators. This leads to improved efficiency and adequate angular sampling, but also to significant multiplexing (MX), which can result in artefacts. We have developed de-MX algorithms that take advantage of the DOI information. We have performed both analytic and Monte Carlo simulations to demonstrate the feasibility, optimize the design and investigate the expected performance of the proposed system. Lesion detectability was preserved with reduction of acquisition time (or radiation dose) by a factor of 2 compared to planar images at the lowest reported dose. The first prototype is under evaluation at Kromek

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

The spatial organization of movement detecting mechanisms in human vision

Imperial Users onl

Infrared Diagnostics on Micro and Nano Scale Structures

Fourier Transform Infrared spectroscopy is used as a diagnostic tool in biological and physical sciences by characterizing the samples based on infrared light-matter interaction. In the case of biological samples, Activation of Jurkat T-cells in culture following treatment with anti-CD3 (Cluster of Differentiation 3) antibody is detectable by interrogating the treated T-cells using the Attenuated Total Reflection - Fourier Transform Infrared (ATR-FTIR) Spectroscopy technique. Cell activation was detected within 75 minutes after the cells encountered specific immunoglobulin molecules. Spectral markers noted following ligation of the CD3 receptor with anti CD3 antibody provides proof-of-concept that ATR-FTIR spectroscopy is a sensitive measure of molecular events subsequent to cells interacting with anti-CD3 Immunoglobulin G (IgG). ATR-FTIR spectroscopy is also used to screen for Colitis in chronic (Interleukin 10 knockout) and acute (Dextran Sodium Sulphate-induced) models. Arthritis (Collagen Antibody Induced Arthritis) and metabolic syndrome (Toll like receptor 5 knockout) models are also tested as controls. The marker identified as mannose uniquely screens and distinguishes the colitic from the non-colitic samples and the controls. The reference or the baseline spectrum could be the pooled and averaged spectra of non-colitic samples or the subject’s previous sample spectrum. The circular dichroism of titanium-doped silver chiral nanorod arrays grown using the glancing angle deposition (GLAD) method is investigated in the visible and near infrared ranges using transmission ellipsometry and spectroscopy. The characteristics of these circular polarization effects are strongly influenced by the morphology of the deposited arrays. Studies of optical phonon modes in nearly defect-free GaN nanowires embedded with intrinsic InGaN quantum dots by using oblique angle transmission infrared spectroscopy is described here. These phonon modes are dependent on the nanowire fill-factor, doping densities of the nanowires and the presence of InGaN dots. These factors can be applied for potential phonon based photodetectors whose spectral responses can be tailored by varying a combination of these three parameters. The optical anisotropy along the growth (c-) axis of the GaN nanowire contributes to the polarization agility of such potential photodetectors

Measuring and simulating haemodynamics due to geometric changes in facial expression

The human brain has evolved to be very adept at recognising imperfections in human skin. In particular, observing someone’s facial skin appearance is important in recognising when someone is ill, or when finding a suitable mate. It is therefore a key goal of computer graphics research to produce highly realistic renderings of skin. However, the optical processes that give rise to skin appearance are complex and subtle. To address this, computer graphics research has incorporated more and more sophisticated models of skin reflectance. These models are generally based on static concentrations of skin chromophores; melanin and haemoglobin. However, haemoglobin concentrations are far from static, as blood flow is directly caused by both changes in facial expression and emotional state. In this thesis, we explore how blood flow changes as a consequence of changing facial expression with the aim of producing more accurate models of skin appearance. To build an accurate model of blood flow, we base it on real-world measurements of blood concentrations over time. We describe, in detail, the steps required to obtain blood concentrations from photographs of a subject. These steps are then used to measure blood concentration maps for a series of expressions that define a wide gamut of human expression. From this, we define a blending algorithm that allows us to interpolate these maps to generate concentrations for other expressions. This technique, however, requires specialist equipment to capture the maps in the first place. We try to rectify this problem by investigating a direct link between changes in facial geometry and haemoglobin concentrations. This requires building a unique capture device that captures both simultaneously. Our analysis hints a direct linear connection between the two, paving the way for further investigatio

Serial sectioning block-face imaging of post-mortem human brain

No current imaging technology can directly and without significant distortion visualize the defining microscopic features of the human brain. Ex vivo histological techniques yield exquisite planar images, but the cutting, mounting and staining they require induce slice-specific distortions, introducing cross-slice differences that prohibit true 3D analysis. Clearing techniques have proven difficult to apply to large blocks of human tissue and cause dramatic distortions as well. Thus, we have only a poor understanding of human brain structures that occur at a scale of 1–100 μm, in which neurons are organized into functional cohorts. To date, mesoscopic features which are critical components of this spatial context, have only been quantified in studies of 2D histologic images acquired in a small number of subjects and/or over a small region of the brain, typically in the coronal orientation, implying that features that are oblique or orthogonal to the coronal plane are difficult to properly analyze. A serial sectioning optical coherence tomography (OCT) imaging infrastructure will be developed and utilized to obtain images of cyto- and myelo-architectural features and microvasculature network of post-mortem human brain tissue. Our imaging infrastructure integrates vibratome with imaging head along with pre and post processing algorithms to construct volumetric OCT images of cubic centimeters of brain tissue blocks. Imaging is performed on tissue block-face prior to sectioning, which preserves the 3D information. Serial sections cut from the block can be subsequently treated with multiplexed histological staining of multiple molecular markers that will facilitate cellular classification or imaged with high-resolution transmission birefringence microscope. The successful completion of this imaging infrastructure enables the automated reconstruction of undistorted volume of human tissue brain blocks and permits studying the pathological alternations arising from diseases. Specifically, the mesoscopic and microscopic pathological alternations, as well as the optical properties and cortical morphological alternations of the dorsolateral prefrontal cortical region of two difference neurodegeneration diseases, Chronic Traumatic Encephalopathy (CTE) and Alzheimer’s Disease (AD), were evaluated using this imaging infrastructure

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment