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Interactive 4-D Cardiac MRI Imaging Based on Iso-Surface Volume Rendering

机译:基于等值面体绘制的交互式4-D心脏MRI成像

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摘要

Cardiac MRI is a technique that provides information about morphology and function of the cardiovascular system in the form of four-dimensional (4-D) scalar data sets. Visualization and extraction of clinically relevant parameters from these data sets may help to diagnose cardiac diseases and malfunctions. Some of these parameters are left (right) ventricle volume, ejection fraction, flow measurements, and wall motion and thickening. Although cardiac MRI is a rapidly growing technique, it must overcome several problems (such as poor spatial resolution, flow and motion artifacts, and low signal-to-noise ratio) in order to produce images with sufficient quality to be used in clinical applications. Existing approaches to visualize cardiac MRI data sets in 4-D are based on rendering a geometrical model extracted from the data. In most cases, these models are polygon meshes describing the epicardial and endo-cardial surfaces of the heart. A wide range of different techniques can be found in the literature to achieve this geometrical model extraction. Our approach consists of applying an iso-surface volume rendering technique in order to visualize the data sets. This visualization includes shape visualization and functional mapping. With this technique, the medical data itself is rendered instead of rendering an extracted geometrical model. This technique has been successfully applied to 3-D MRI and CT data sets. Even though the extension of this technique to 4-D data sets is not straightforward, the preliminary results are very promising.
机译:心脏MRI是一种以四维(4-D)标量数据集的形式提供有关心血管系统形态和功能的信息的技术。从这些数据集中可视化和提取临床相关参数可能有助于诊断心脏疾病和功能障碍。其中一些参数是左(右)心室容积,射血分数,流量测量值以及壁运动和增厚。尽管心脏MRI是一项快速发展的技术,但它必须克服几个问题(例如较差的空间分辨率,流动和运动伪像以及较低的信噪比),以产生具有足够质量的图像以用于临床应用。可视化4-D心脏MRI数据集的现有方法基于渲染从数据中提取的几何模型。在大多数情况下,这些模型是描述心脏的心外膜和心内膜表面的多边形网格。在文献中可以找到多种不同的技术来实现这种几何模型的提取。我们的方法包括应用等值面体绘制技术以可视化数据集。该可视化包括形状可视化和功能映射。使用此技术,可以呈现医疗数据本身,而不是呈现提取的几何模型。该技术已成功应用于3-D MRI和CT数据集。即使将该技术扩展到4-D数据集并不简单,但初步结果还是很有希望的。

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