1,439 research outputs found
REAL-TIME 4D ULTRASOUND RECONSTRUCTION FOR IMAGE-GUIDED INTRACARDIAC INTERVENTIONS
Image-guided therapy addresses the lack of direct vision associated with minimally- invasive interventions performed on the beating heart, but requires effective intraoperative imaging. Gated 4D ultrasound reconstruction using a tracked 2D probe generates a time-series of 3D images representing the beating heart over the cardiac cycle. These images have a relatively high spatial resolution and wide field of view, and ultrasound is easily integrated into the intraoperative environment. This thesis presents a real-time 4D ultrasound reconstruction system incorporated within an augmented reality environment for surgical guidance, whose incremental visualization reduces common acquisition errors. The resulting 4D ultrasound datasets are intended for visualization or registration to preoperative images. A human factors experiment demonstrates the advantages of real-time ultrasound reconstruction, and accuracy assessments performed both with a dynamic phantom and intraoperatively reveal RMS localization errors of 2.5-2.7 mm, and 0.8 mm, respectively. Finally, clinical applicability is demonstrated by both porcine and patient imaging
Initial experience using contrast enhanced real-time three-dimensional exercise stress echocardiography in a low-risk population
Although emerging data support the utility of real-time three-dimensional echocardiography (RT3DE) during dobutamine stress testing, the feasibility of performing contrast enhanced RT3DE during exercise treadmill stress has not been explored. Two-dimensional (2D) and three-dimensional (3D) acquisition were performed in 39 patients at rest and peak exercise. Contrast was used in 29 patients (74%). Reconstruction was performed manually by generating short axis cut planes at the base, mid-ventricle and apex, and automatically by generating 9 short axis slices. Three-dimensional acquisition was feasible during rest and stress regardless of the use of contrast. Time to acquire stress images was reduced using 3D (35.2±17.9 s) as compared to 2D acquisition (51.6±14.7 s; P<0.05). Using a 17-segment model, of all 663 segments, 588 resting (88.6%) and 563 stress segments (84.9%) were adequately visualized using manually reconstructed 3D data, compared with 618 resting (93.2%) and 606 stress segments (91.4%) using 2D data (P rest=0.06; P stress=0.07). We concluded that contrast enhanced RT3DE is feasible during treadmill stress echocardiography
Automated Analysis of 3D Stress Echocardiography
__Abstract__
The human circulatory system consists of the heart, blood, arteries, veins and
capillaries. The heart is the muscular organ which pumps the blood through the
human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium
into the right ventricle, which pumps the blood into the pulmonary arteries. The
blood is carried to the lungs, where it passes through a capillary network that
enables the release of carbon dioxide and the uptake of oxygen. Oxygenated
blood then returns to the heart via the pulmonary veins and flows from the left
atrium into the left ventricle. The left ventricle then pumps the blood through the
aorta, the major artery which supplies blood to the rest of the body [Drake et a!.,
2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system
remains healthy. Disease of the cardiovascular system, if untreated, ultimately
leads to the failure of other organs and death
Cardiovascular imaging 2011 in the International Journal of Cardiovascular Imaging
Cardiovascular Aspects of Radiolog
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Hierarchical template matching for 3D myocardial tracking and cardiac strain estimation
Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers
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