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Deformable and Rigid Model-Based Image Registration for Quantitative Cardiac Perfusion

机译:基于变形和刚性模型的定量心脏灌注图像配准

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Background: Inter-frame image registration is a major hurdle in accurate quantification of myocardial perfusion using MRI. The registration is not standard, in that changing contrast between frames makes it difficult to register the images automatically. Methods: A multiple step approach was employed. First, a region around the heart was identified out automatically in order to focus the registration. Then we performed rigid shifts between frames with a cross correlation type of method, to obtain a coarse registration. Then we created model images from a two compartment model and an arterial input function from the RV blood pool of the images. These model images represent the uptake and washout of the contrast agent. However they do not contain any motion since the two compartment motion cannot explicitly model motion. These motion-free model images are used as reference images and each frame was registered to its associated model image. Rigid and deformable registration as implemented by ANTS. The entire process was automatic and required ~240 seconds. This registration approach was tested on the 10 provided ECG-gated rest/stress datasets. Conclusion: Rigid and deformable registration was performed on the provided datasets. The technique was found to perform better on datasets with higher signal to noise ratio and without sudden respiratory motions.
机译:背景:帧间图像配准是使用MRI准确定量心肌灌注的主要障碍。配准不是标准的,因为帧之间对比度的变化使自动配准图像变得困难。方法:采用多步骤方法。首先,自动识别出心脏周围的区域,以使配准集中。然后,我们使用互相关类型的方法在帧之间执行刚性平移,以获得粗略配准。然后,我们从两个隔室模型创建模型图像,并从图像的RV血库创建动脉输入函数。这些模型图像代表了造影剂的吸收和洗脱。但是,它们不包含任何运动,因为两个隔室的运动无法显式建模运动。这些无运动的模型图像用作参考图像,并且每个帧都已注册到其关联的模型图像。由ANTS实施的刚性且可变形的配准。整个过程是自动的,大约需要240秒。在提供的10个ECG门控休息/压力数据集上测试了这种注册方法。结论:对提供的数据集进行了刚性和可变形的配准。发现该技术在具有较高信噪比且没有突然呼吸运动的数据集上表现更好。

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