matlab - Interpolation between two images with different pixelsize

Question

For my application, I want to interpolate between two images(CT to PET). Therefore I map between them like that:

[X,Y,Z] = ndgrid(linspace(1,size(imagedata_ct,1),size_pet(1)),...
                linspace(1,size(imagedata_ct,2),size_pet(2)),...
                linspace(1,size(imagedata_ct,3),size_pet(3)));
new_imageData_CT=interp3(imagedata_ct,X,Y,Z,'nearest',-1024);

The size of my new image new_imageData_CT is similar to PET image. The problem is that data of my new image is not correct scaled. So it is compressed. I think the reason for that is that the pixelsize between the two images is different and not involved to the interpolation. So for example :

• CT image size : 512x512x1027
• CT voxel size[mm] : 1.5x1.5x0.6
• PET image size : 192x126x128
• PET voxel size[mm] : 2.6x2.6x3.12

So how could I take care about the voxel size regarding to the interpolation?

Answer 1

You need to perform a matching in the patient coordinate system, but there is more to consider than just the resolution and the voxel size. You need to synchronize the positions (and maybe the orientations also, but this is unlikely) of the two volumes.

You may find this thread helpful to find out which DICOM Tags describe the volume and how to calculate transformation matrices to use for transforming between the patient (x, y, z in millimeters) and volume (x, y, z in column, row, slice number).

You have to make sure that the volume positions are comparable as the positions of the slices in the CT and PET do not necsesarily refer to the same origin. The easy way to do this is to compare the DICOM attribute Frame Of Reference UID (0020,0052) of the CT and PET slices. For all slices that share the same Frame Of Reference UID, the position of the slice in the DICOM header refers to the same origin. If the datasets do not contain this tag, it is going to be much more difficult, unless you just put it as an assumption. There are methods to deduce the matching slices of two different volumes from the contents of the pixel data referred to as "registration" but this is a science of its own. See the link from Hugues Fontenelle.

BTW: In your example, you are not going to find a matching voxel in both volumes for each position as the volumes have different size. E.g. for the x-direction:

CT: 512 * 1.5 = 768 millimeters

PET: 192 * 2.6 = 499 millimeters