我想注册两个体积图像脑（PET和CT甚至PET和MR）。每个体积图像中包含不同数量的2D图像（片）的。 例如， CT 的 150 片和 PET 的 100 片。我想用插值法计算，降低CT片数为100。这是一个正确的做法吗？有谁知道这可能是对我有帮助的任何资源？像一个伪code或步骤，我应该通过登记2体积图像。 谢谢：）

I am trying to register two volumetric images from brain (PET and CT or even PET and MR). Each of these volumetric images contains different numbers of 2D images (slices). For example, CT has 150 slices and PET has 100 slices. I was thinking of using an interpolation method to calculate and reduce the number of CT slices to 100. Is this a correct approach? Does anyone know of any resources that could be helpful for me? like a pseudo code, or steps that I should go through for registering two volumetric images. Thank you :)

推荐答案

如果您知道的间距信息为150 CT片和100 PET切片，你可以看看MATLAB的的 interp1 功能沿一个轴插补重新调整图像到相同数目的像素。从这里也许可以使用MATLAB的 imregister 进行注册。

If you know the spacing information for the 150 CT slices and the 100 PET slices, you can look into MATLAB's interp1 function for interpolating along one axis to rescale the images to the same number of pixels. From here it might be possible to use MATLAB's imregister to perform registration.

如果您想了解登记引擎盖下是如何工作的（像素和物理坐标之间的转换，转换/重采样图像等），一个资源我可以直接到是的 ITK软件指南PDF 。

If you are looking to learn how registration works under the hood (transforming between pixel and physical coordinates, transforming/resampling images, etc.), one resource I can direct you to is the ITK Software Guide pdf.

在特定的，试对图像读取册第4.1.4节（第41页的PDF）再presentation，和2册第3.9节（PDF的页532）上的转换。

In particular, try reading Book 1 Section 4.1.4 (page 41 of the pdf) on image representation, and Book 2 Section 3.9 (page 532 of the pdf) on transforms.

在一般情况下，转化和在登记的三维图像内插的问题可以是pretty的繁琐写入code表示。你需要问自己有关的间距和像素的方向，如何转换和插值图像，使它们的网格重叠的，而且你还需要决定如何处理在网格像素进行评估时，相似性度量摆在图像边界之外

In general, the problem of transforming and interpolating with 3D images in registration can be pretty cumbersome to write code for. You need to ask yourself about the spacing and orientation of pixels, how to transform and interpolate images so that their grids overlap, and you also need to decide what to do with pixels in your grid that lie outside the image boundary when evaluating the similarity metric.

虽然它给你做你认为是最好的，我建议你使用现有的注册程序，如果他们能够做到的事情你想要的：

While it's up to you to do what you think is best, I suggest you use existing registration programs if they are capable of doing what you want:

在MATLAB的 imregister （我从来没有使用过，所以我不能对此发表评论） simpleITK 为Python 的ITK对C ++ 有一个学习曲线，但给出了完全的控制权登记程序 的Elastix 是使用的参数的文本文件进行注册一个命令行程序。 3D切片机具有简单线性的注册图形用户界面 MATLAB's imregister (I have never used it so I can't comment on it) simpleITK for Python the ITK for C++ has a learning curve but gives full control over the registration process elastix is a command line program that uses a text file of parameters to perform registration. 3D slicer has a graphical user interface for simple linear registration