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It is left to the binding image of silk to hold everything together.
The specific binding detected in each structure was quantified by subtracting the non-specific binding image from the total binding image.
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The resulting transformation matrix was applied to the corresponding binding images.
Spatially normalized binding images from eight independent healthy subjects were averaged to obtain a high SNR raclopride PET template.
One-step co-registration of raclopride binding images to the binding template may also reduce the risks of errors introduced in the multi-step MR-based process.
Static raclopride PET binding images from 20 patients with suspected parkinsonian syndrome were normalized to MNI space using the PET template.
Individual raclopride binding images from healthy volunteer dynamic data were generated by averaging the PET frames acquired between 40 and 60 min after tracer injection.
Raclopride binding images were directly transformed into MNI space by normalization to the raclopride PET template with the same parameters as described for the MRI normalization.
However, the need for MR images and full dynamic PET series in addition to receptor binding images from late time frames in PET is not cost-effective in a clinical setting.
The transformation matrices of the rigid registration of PET to MRI and the elastic registration of MRI to stereotaxic MNI space were combined to normalize the PET binding images into the MNI space.
Specific binding images were generated by automatically co-registering non-specific binding images to total binding images using VTK Cisg Registration Toolkit (VTKCisg, London, UK), then subtracting the non-specific binding images from total binding images using a MatLab™ script (MathWorks Inc., Cambridge, UK).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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