Purpose: The purpose of this paper is to present a methodology to estimate the carotid artery lumen centerlines in ultrasound (US) images obtained in a free-hand examination. Challenging aspects here are speckle noise in US images, artifacts, and the lack of contrast in the direction orthogonal to the US beam direction.
Method: An algorithm based on a rough lumen segmentation obtained by robust ellipse fitting was developed to deal with these conditions and estimate the lumen center in 2D B-mode scans. In a free-hand sweep examination, continuous image acquisitions are performed through time when the radiologist moves the probe on the patient's neck. The result is a series of images that show 2D cross-sections of the carotid's morphology. A tracking sensor (Flock of Birds) was attached to the probe and both were connected to a PC executing the Stradwin software, which relates spatial information to the acquisition data of the US probe. The spatial information was combined with the 2D lumen center estimates to provide a centerline in 3D. For validation, 19 carotid scans from 15 different patients were scanned, their centerlines calculated by the algorithm and compared with results acquired by manual annotations.
Results: The average Euclidean distance between both among all the examinations was 0.82 mm. For each examination, the percentage of these Euclidean distances below 2 mm was calculated; the average over all examinations was 92%.
Conclusion: Automated 3D estimation of carotid artery lumen centerlines in free-hand real-time ultrasound is feasible and can be performed with high accuracy. The algorithm is robust enough to keep the centerlines inside the vessel, even in the absence of contrast in parts of the vessel wall.
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http://dx.doi.org/10.1007/s11548-011-0633-x | DOI Listing |
Int J Numer Method Biomed Eng
January 2025
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Modeling fibrous tissue for vascular fluid-structure interaction analysis poses significant challenges due to the lack of effective tools for preparing simulation data from medical images. This limitation hinders the physiologically realistic modeling of vasculature and its use in clinical settings. Leveraging an established lumen modeling strategy, we propose a comprehensive pipeline for generating thick-walled artery models.
View Article and Find Full Text PDFCirc J
December 2024
Department of Cardiovascular Medicine, Gifu Heart Center.
Background: The angiography-derived non-hyperemic pressure ratio (angioNHPR) is a novel index of NHPR based on artificial intelligence (AI) that does not require pressure wires. We investigated the diagnostic accuracy of angioNHPR for detecting hemodynamically relevant coronary artery disease.
Methods And Results: In this retrospective single-center study, angioNHPR was assessed using the invasive NHPR as the reference standard.
J Vasc Surg
November 2024
Cleveland Clinic Foundation, Cleveland, OH. Electronic address:
Microcirculation
November 2024
Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan.
Objective: This study aimed to examine the spatiotemporal coherence of capillary lumen fluctuations in relation to spatial variations in the pericyte lining in the cortex of anesthetized mice.
Methods: Two-photon microscopic angiography data (previously published) were reanalyzed, and spatial variations in capillary diameter fluctuations at rest and in capillary lining with vascular mural cells were measured along capillary centerlines.
Results: Relatively large diameters of the capillaries (5.
J Magn Reson Imaging
August 2024
Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Background: Currently available tools for noninvasive motility quantification of the small intestine are limited to dynamic 2D MRI scans, which are limited in their ability to differentiate between types of intestinal motility.
Purpose: To develop a method for quantification and characterization of small intestinal motility in 3D, capable of differentiating motile, non-motile and peristaltic motion patterns.
Study Type: Prospective.
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