Abstract
A Fourier Transform (FT) based pattern-matching algorithm was adapted for use in medical image registration. This algorithm obtained the FT of two images, determined the normalized cross-power spectrum of the transformed images, and then applied an inverse FT. The result was a delta function with a maximum value at the location corresponding to the distance between the two images; a similar method was used to recover rotations. This algorithm was first tested using a simple two-dimensional image, with induced shifts of ±20 pixels and ±10 degrees. All translations were recovered with no error and all rotations were recovered within 0.18 degrees. Subsequently, this algorithm was tested on eight clinical kV images drawn from four different body sites. Twenty-five random shifts and rotations were applied to each image. The average mean error of the registration solution was -0.002 ± 0.077 mm in the x direction, 0.002 ± 0.075 mm in the y direction, and -0.012 ± 0.099 degrees. These initial results suggest that a FT algorithm has a high degree of accuracy when registering clinical kV images.
Highlights
With the advent of modern radiotherapy techniques, such as intensity modulated radiation therapy (IMRT) and stereotactic body radiotherapy (SBRT), increased importance is placed on accurate and reproducible positioning of the patient on a daily basis
The purpose of this study is to examine the efficacy of using a Fourier Transform (FT)-based image registration algorithm for the alignment of daily kV images used for image guidance
It should be noted that the discrete nature of the registration algorithm presented here limits the accuracy of the algorithm to the pixel size of the image description (For an example of an algorithm designed for sub-pixel registration, see [18])
Summary
With the advent of modern radiotherapy techniques, such as intensity modulated radiation therapy (IMRT) and stereotactic body radiotherapy (SBRT), increased importance is placed on accurate and reproducible positioning of the patient on a daily basis. An important component of image guidance is the ability to register or align images obtained on the treatment unit with those produced at the time of planning, in order to determine the translations and/or rotations to place the patient in the desired position. More automated, methods have been developed which attempt to use the information in the entire image, or some selected sub-area, to guide the image registration process. One such method determines the mutual information (MI) between the two images and iteratively determines the location where they show the highest degree of correlation [3,4]. This method has shown good results, with the caveat that the accuracy of the solution is dependent on the correct choice of the threshold used to guide the iterative processes in the algorithm
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