Image segmentation and reconstruction of 3D surfaces from carotid ultrasound images

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Quantitative Analysis of Ultrasound Images of the Preterm Brain

In this PhD new algorithms are proposed to better understand and diagnose white matter damage in the preterm Brain. Since Ultrasound imaging is the most suited modality for the inspection of brain pathologies in very low birth weight infants we propose multiple techniques to assist in what is called Computer-Aided Diagnosis. As a main result we are able to increase the qualitative diagnosis from a 70% detectability to a 98% quantitative detectability

Engineering a 3D ultrasound system for image-guided vascular modelling

Atherosclerosis is often diagnosed using an ultrasound (US) examination in the carotid and femoral arteries and the abdominal aorta. A decision to operate requires two measures of disease severity: the degree of stenosis measured using B-mode US; and the blood flow patterns in the artery measured using spectral Doppler US. However other biomechanical factors such as wall shear stress (WSS) and areas of flow recirculation are also important in disease development and rupture. These are estimated using an image-guided modelling approach, where a three-dimensional computational mesh of the artery is simulated. To generate a patient-specific arterial 3D computational mesh, a 3D ultrasound (3DUS) system was developed. This system uses a standard clinical US scanner with an optical position sensor to measure the position of the transducer; a video capture card to record video images from the scanner; and a PC running Stradwin software to reconstruct 3DUS data. The system was characterised using an industry-standard set of calibration phantoms, giving a reconstruction accuracy of ± 0.17 mm with a 12MHz linear array transducer. Artery movements from pulsatile flow were reduced using a retrospective gating technique. The effect of pressure applied to the transducer moving and deforming the artery was reduced using an image-based rigid registration technique. The artery lumen found on each 3DUS image was segmented using a semi-automatic segmentation technique known as ShIRT (the Sheffield Image Registration Toolkit). Arterial scans from healthy volunteers and patients with diagnosed arterial disease were segmented using the technique. The accuracy of the semi-automatic technique was assessed by comparing it to manual segmentation of each artery using a set of segmentation metrics. The mean accuracy of the semi-automatic technique ranged from 85% to 99% and depended on the quality of the images and the complexity of the shape of the lumen. Patient-specific 3D computational artery meshes were created using ShIRT. An idealised mesh was created using key features of the segmented 3DUS scan. This was registered and deformed to the rest of the segmented dataset, producing a mesh that represents the shape of the artery. Meshes created using ShIRT were compared to meshes created using the Rhino solid modelling package. ShIRT produced smoother meshes; Rhino reproduced the shape of arterial disease more accurately. The use of 3DUS with image-guided modelling has the potential to be an effective tool in the diagnosis of atherosclerosis. Simulations using these data reflect in vivo studies of wall shear stress and recirculation in diseased arteries and are comparable with results in the literature created using MRI and other 3DUS systems

Non-invasive ultrasound monitoring of regional carotid wall structure and deformation in atherosclerosis

Thesis (Ph. D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 2001.Includes bibliographical references (p. 223-242).Atherosclerosis is characterized by local remodeling of arterial structure and distensibility. Developing lesions either progress gradually to compromise tissue perfusion or rupture suddenly to cause catastrophic myocardial infarction or stroke. Reliable measurement of changes in arterial structure and composition is required for assessment of disease progression. Non-invasive carotid ultrasound can image the heterogeneity of wall structure and distensibility caused by atherosclerosis. However, this capability has not been utilized for clinical monitoring because of speckle noise and other artifacts. Clinical measures focus instead on average wall thickness and diameter distension in the distal common carotid to reduce sensitivity to noise. The goal of our research was to develop an effective system for reliable regional structure and deformation measurements since these are more sensitive indicators of disease progression. We constructed a system for freehand ultrasound scanning based on custom software which simultaneously acquires real-time image sequences and 3D frame localization data from an electromagnetic spatial localizer. With finite element modeling, we evaluated candidate measures of regional wall deformation.(cont.) Finally, we developed a multi-step scheme for robust estimation of local wall structure and deformation. This new strategy is based on a directionally-sensitive segmentation functional and a motion-region-of-interest constrained optical flow algorithm. We validated this estimator with simulated images and clinical ultrasound data. The results show structure estimates that are accurate and precise, with inter- and intra-observer reproducibility surpassing existing methods. Estimates of wall velocity and deformation likewise show good overall accuracy and precision. We present results from a proof-of-principle evaluation conducted in a pilot study of normal subjects and clinical patients. For one example, we demonstrate the combination of 2D image processing with 3D frame localization for visualization of the carotid volume. With slice localization, estimates of carotid wall structure and deformation can be derived for all axial positions along the carotid artery. The elements developed here provide the tools necessary for reliable quantification of regional wall structure and composition changes which result from atherosclerosis.by Raymond C. Chan.Ph.D

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods