Area-preserving mapping of 3D ultrasound carotid artery images using density-equalizing reference map

Carotid atherosclerosis is a focal disease at the bifurcations of the carotid artery. To quantitatively monitor the local changes in the vessel-wall-plus-plaque thickness (VWT) and compare the VWT distributions for different patients or for the same patients at different ultrasound scanning sessions, a mapping technique is required to adjust for the geometric variability of different carotid artery models. In this work, we propose a novel method called density-equalizing reference map (DERM) for mapping 3D carotid surfaces to a standardized 2D carotid template, with an emphasis on preserving the local geometry of the carotid surface by minimizing the local area distortion. The initial map was generated by a previously described arc-length scaling (ALS) mapping method, which projects a 3D carotid surface onto a 2D non-convex L-shaped domain. A smooth and area-preserving flattened map was subsequently constructed by deforming the ALS map using the proposed algorithm that combines the density-equalizing map and the reference map techniques. This combination allows, for the first time, one-to-one mapping from a 3D surface to a standardized non-convex planar domain in an area-preserving manner. Evaluations using 20 carotid surface models show that the proposed method reduced the area distortion of the flattening maps by over 80% as compared to the ALS mapping method

Analysis of carotid lumen surface morphology using three-dimensional ultrasound imaging

Carotid plaque surface irregularity and ulcerations play an important role in the risk of ischemic stroke. Ulcerated or fissured plaque, characterized by irregular surface morphology, exposes thrombogenic materials to the bloodstream, possibly leading to life- or brain-threatening thrombosis and embolization. Therefore, the quantification of plaque surface irregularity is important to identify high-risk plaques that would likely lead to vascular events. Although a number of studies have characterized plaque surface irregularity using subjective classification schemes with two or more categories, only a few have quantified surface irregularity using an objective and continuous quantity, such as Gaussian or mean curvature. In this work, our goal was to use both Gaussian and mean curvatures for identifying ulcers from 3D carotid ultrasound (US) images of human subjects. Before performing experiments using patient data, we verified the numerical accuracy of the surface curvature computation method using discrete spheres and tori with different sampling intervals. We also showed that three ulcers of the vascular phantom with 2 mm, 3 mm and 4 mm diameters were associated with high Gaussian and mean curvatures, and thus, were easily detected. Finally, we demonstrated the application of the proposed method for detecting ulcers on luminal surfaces, which were segmented from the 3D US images acquired for two human subjects

Three-dimensional segmentation of three-dimensional ultrasound carotid atherosclerosis using sparse field level sets.

PURPOSE: Three-dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility. METHODS: The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors\u27 knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user-specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional- and boundary-based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region-based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region-based information and the anchor points. The algorithm-generated surfaces were sliced and evaluated with respect to manual segmentations on a slice-by-slice basis using 21 3DUS images. RESULTS: The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm-generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image. CONCLUSIONS: The proposed 3D semiautomated segmentation algorithm yielded high-accuracy and high-repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Hyperpolarized 3He Magnetic Resonance Imaging Phenotypes of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the world. Identifying clinically relevant COPD phenotypes has the potential to reduce the global burden of COPD by helping to alleviate symptoms, slow disease progression and prevent exacerbation by stratifying patient cohorts and forming targeted treatment plans. In this regard, quantitative pulmonary imaging with hyperpolarized 3He magnetic resonance imaging (MRI) and thoracic computed tomography (CT) have emerged as ways to identify and measure biomarkers of lung structure and function. 3He MRI may be used as a tool to probe both functional and structural properties of the lung whereby static-ventilation maps allow the direct visualization of ventilated lung regions and 3He apparent diffusion coefficient maps show the lung microstructure at alveolar scales. At the same time, thoracic CT provides quantitative measurements of lung density and airway wall and lumen dimensions. Together, MRI and CT may be used to characterize the relative contributions of airways disease and emphysema on overall lung function, providing a way to phenotype underlying disease processes in a way that conventional measurements of airflow, taken at the mouth, cannot. Importantly, structure-function measurements obtained from 3He MRI and CT can be extracted from the whole-lung or from individual lung lobes, providing direct information on specific lung regions. In this thesis, my goal was to identify pulmonary imaging phenotypes to provide a better understanding of COPD pathophysiology in ex-smokers with and without airflow limitation. This thesis showed: 1) ex-smokers without airflow limitation had imaging evidence of subclinical lung and vascular disease, 2) pulmonary abnormalities in ex- smokers without airflow limitation were spatially related to airways disease and very mild emphysema, and, 3) in ex-smokers with COPD, there were distinct apical-basal lung phenotypes associated with disease severity. Collectively, these findings provide strong evidence that quantitative pulmonary imaging phenotypes may be used to characterize the underlying pathophysiology of very mild or early COPD and in patients with severe disease

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

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on