I need to register a 3d structural T1 image to 4D dwi image. However, flirt turns out an error due to its applicability to 3d data only. Which method should I use?
Thanks,
Rosella
Hi Rosella,
This is kind of fundamental to how to properly think about registration, so I’ll try to communicate the way I think about it, since it probably applies beyond the context of this specific question. It’s not about avoiding a command error so much as understanding the underlying processes. We really should add an FAQ page about registration / transformation / regridding at some point though…
Imagine that you are the flirt command. Your job is to receive two images as input, and try to move one image around in 3D space so that it “lines up” with the other.
Now imagine that somebody walks along and says “register these two images for me”. You look at what they’ve given you: image 1 is a 3D image and image 2 is a 4D image.
What do you do?
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For each of the 3D volumes in image 2, perform a registration with image 1. You end up with as many transformations as there are volumes in image 2. Probably doesn’t make a whole lot of sense.
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Assume that the first volume in image 2 is “representative” of the whole contents of image 2; extract just that volume, and do the registration with image 1.
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Decide that you want to use all of the contents of image 2; so you calculate e.g. the mean statistic over the volumes in image 2 to produce a 3D volume, and do the registration with image 1.
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Tell this person that they are not interacting with you appropriately; your job is ambiguous.
Sounds like the FSL developers have chosen option 4. So did we.
So the question to you is: What do you want flirt to do? Well, you probably want it to “align these two images as best as possible”. Well, what does that involve? It involves computing some “similarity metric” that quantifies how well the two images are aligned, and tries to optimize the value of that function as much as possible. So what do you need to provide to flirt in order for it to be able to do its job properly? Well, it needs two images, each of which contains some kind of contrast, such that whatever similarity metric is employed has a drastically different value when the images are well-aligned to when they are not.
What is this image in the case of the 4D DWI? Well, it could be a mean b=0 image, it could be an FA image, it could be a pseudo-T1-weighted image, it could be a WM ODF l=0 image; whatever you think is going to provide flirt with the best capability to co-localise the biological spatial information contained within those two images as best as possible.
There may well be a discussion around exactly what image is best; but from the way you posed the question I wanted to make sure that the reason why this is even an issue is understood.
Cheers
Rob
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