Image interpolation is inextricable in many image analysis steps including registration of low resolution images to a standard high resolution template. Interpolated images are often smoothed and their voxel intensities are not statistically independent which severely complicates subsequent statistical analysis at the group level. Difficult to account correlations lead to higher than expected false-positive rates. We propose a new, noise-fill, image interpolation method which avoids both spatial blurring and loss of statistical independence of the voxel intensities. We show that noise-fill interpolation improves sensitivity of lesion detection in simulated patient-specific voxel-wise cluster analyses compared to other typically used interpolation methods.
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