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首页> 外文会议>2002 6th International Conference on Signal Processing Proceedings (ICSP'02) Vol.2; Aug 26-30, 2002; Beijing, China >Signal Subspace Registration of Time Series Medical Imagery
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Signal Subspace Registration of Time Series Medical Imagery

机译：时间序列医学影像的信号子空间配准

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摘要

Image registration is one of the crucial steps in detecting changes among the time series medical images. Due to variations in the imaging system over time, the impulse response of the imaging system, also known as its Point Spread Function (PSF), exhibits a time-varying behavior. The registration is further complicated due to the subtle coordinate changes introduced by the patient. In this work, the registration problem is approached via a spatially varying multi-dimensional adaptive filtering method that relates one image in terms of an unknown linear combination of the other image and its spatially transformed versions. Using this model, we develop a scheme, which we refer to as Signal Subspace Processing, to estimate a localized impulse response to calibrate relatively small regions. A criterion is designed to identify the localized PSF's that are not sensitive to the system noise or anatomical changes but accurately represent the spatially varying nature of the unknown miscalibration sources. Low order polynomials are used to sew the localized PSF together and construct a global spatially variant PSF. The anatomical changes between the time series images are achieved by calibrating the image with the global spatially variant PSF. Numerical experiments iising MR images illustrate the effectiveness of the proposed algorithm.
机译：图像配准是检测时间序列医学图像之间变化的关键步骤之一。由于成像系统随时间的变化，成像系统的脉冲响应（也称为点扩展函数（PSF））表现出随时间变化的行为。由于患者引入的细微坐标变化，配准更加复杂。在这项工作中，配准问题是通过空间变化的多维自适应滤波方法解决的，该方法将一个图像根据另一幅图像及其空间变换版本的未知线性组合进行关联。使用此模型，我们开发了一种方案，称为信号子空间处理，以估计局部脉冲响应以校准相对较小的区域。设计一个标准来识别对系统噪声或解剖变化不敏感，但准确表示未知失标源的空间变化性质的局部PSF。低阶多项式用于将局部PSF缝制在一起，并构造全局空间变体PSF。时间序列图像之间的解剖变化是通过使用全局空间变异PSF校准图像来实现的。 MR图像仿真实验表明了该算法的有效性。

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