title: Carotid plaque 3D compound imaging and echo-morphology analysis: a Bayesian approach.
abstract: This paper describes a method for volume reconstruction of the carotid plaque and presents a novel local characterization of its echo-morphology. The data is composed by a series of nearly parallel ultrasound images (3D Compound Imaging) and the acquisition is performed using traditional non-invasive ultrasound equipment available in most medical facilities, without need of a spatial locator device. The reconstruction algorithm uses the observed pixels inside the plaque, which were obtained in a pre-segmentation stage performed under medical guidance [1]. The paper proposes a Bayesian algorithm which estimates the underlying volume inside the plaque, by filtering and interpolating the data in order to remove speckle noise and fill non-observed regions, respectively. This volume is further used in plaque echo-morphology analysis. The observation model is based on the Rayleigh distribution, commonly used to model speckle noise in ultrasound images. A prior model based on the edge preserving Total Variation Gibbs distribution is also used to fill the gaps on non-evenly spaced observations. An energy function is derived from these models and an iterative algorithm computes its minimizer. The estimated function, defined in a given volume of interest, is used in global and local plaque characterization, namely to estimate its average levels of stenosis, echo-morphology and to identify vulnerable foci inside the plaque. The goal is to make atherosclerosis diagnosis more accurate and complete than using traditional 2D ultrasound analysis.
authors.affiliation: Institute of Systems and Robotics, Technical University of Lisbon, Portugal. jseabra@isr.ist.utl.pt
authors.forename: J C
authors.initials: JC
authors.lastname: Seabra
authors.forename: J M
authors.initials: JM
authors.lastname: Sanches
authors.forename: L M
authors.initials: LM
authors.lastname: Pedro
authors.forename: J F
authors.initials: JF
authors.lastname: e Fernandes
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002339:Carotid Arteries / Q000000981:diagnostic imaging; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008955:Models, Cardiovascular; D014463:Ultrasonography