Abstract
Image registration serves many applications in medical imaging, including longitudinal studies, treatment verification, and more recently, morphometry. Registration processing is regularly applied in magnetic resonance (MR) images, where imaging is highly adaptable in capturing soft tissue contrast. To obtain the greatest registration accuracy in MR imaging, the inherent imaging tradeoff between SNR and resolution at a given scan time should be optimized for computational accuracy, rather than human viewing. We investigated this SNR-resolution tradeoff to optimize registration for digital morphometry. Tradeoff images were simulated from acquired gold standard MR images to emulate a shorter, constant acquisition time, but at the expense of SNR, resolution, or both. The group of images from each tradeoff was nonlinearly registered toward an average atlas producing deformation fields, useful for identifying differences in morphology. The gold standard data were also registered. The deformation fields were used to evaluate registration performance of each tradeoff relative to the gold standard. For fixed scan times, the optimal SNR for registration with MR imaging was found to be approximately 20. Image resolution should be adjusted to produce this target voxel SNR when registration is a central processing task.