Resampling 4D images using adaptive filtering

Alexander Andreopoulos, John K. Tsotsos
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Abstract

We present an adaptive filtering based methodology for resampling 3D time series images using an extension of the method presented by simultaneously reducing the artifacts due to image noise and resample the data on a finer grid along the time dimension. This provides a methodology for obtaining high quality image resampling without the disadvantages of staircase artifacts created by more common interpolation methods such as linear interpolation. We present qualitative results of the algorithm on a data set of 4D cardiac MRI. This is a useful approach for any situation where we have a data set of 4D images needing to be resampled.
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使用自适应滤波对4D图像进行重采样
我们提出了一种基于自适应滤波的方法,用于对3D时间序列图像进行重采样,该方法是对该方法的扩展,同时减少了由于图像噪声引起的伪影,并沿着时间维度在更细的网格上对数据进行重采样。这提供了一种获得高质量图像重采样的方法,而没有更常见的插值方法(如线性插值)产生的阶梯伪影的缺点。我们在4D心脏MRI数据集上给出了该算法的定性结果。这是一个有用的方法,对于任何情况下,我们有一个数据集的4D图像需要重新采样。
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