Robust Cardiac Function Assessment in 4D PC-MRI Data of the Aorta and Pulmonary Artery

Periodicals(2016)

引用 18|浏览19
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摘要
AbstractFour-dimensional phase-contrast magnetic resonance imaging 4D PC-MRI allows the non-invasive acquisition of time-resolved, 3D blood flow information. Stroke volumes SVs and regurgitation fractions RFs are two of the main measures to assess the cardiac function and severity of valvular pathologies. The flow rates in forward and backward direction through a plane above the aortic or pulmonary valve are required for their quantification. Unfortunately, the calculations are highly sensitive towards the plane's angulation since orthogonally passing flow is considered. This often leads to physiologically implausible results. In this work, a robust quantification method is introduced to overcome this problem. Collaborating radiologists and cardiologists were carefully observed while estimating SVs and RFs in various healthy volunteer and patient 4D PC-MRI data sets with conventional quantification methods, that is, using a single plane above the valve that is freely movable along the centerline. By default it is aligned perpendicular to the vessel's centerline, but free angulation rotation is possible. This facilitated the automation of their approach which, in turn, allows to derive statistical information about the plane angulation sensitivity. Moreover, the experts expect a continuous decrease of the blood flow volume along the vessel course. Conventional methods are often unable to produce this behaviour. Thus, we present a procedure to fit a monotonous function that ensures such physiologically plausible results. In addition, this technique was adapted for the usage in branching vessels such as the pulmonary artery. The performed informal evaluation shows the capability of our method to support diagnosis; a parameter evaluation confirms the robustness. Vortex flow was identified as one of the main causes for quantification uncertainties.
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关键词
scientific visualization,4d pc-mri,stroke volume,quantification,I,4,9 [Computing Methodologies]: Image Processing and Computer Vision Applications
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