UPsampling by Subspace Informed ZEro-padded Reconstruction (UPSIZER) in Diffusion Tensor Imaging

Motivation: Diffusion tensor imaging (DTI) is inherently resolution limited by MRI gradient performance and human tolerance of gradient slew rates but could benefit greatly from finer detail for tissue mapping. Goal(s): To increase the resolution of DTI images by using the joint information between different encoding directions to improve efficiency of upsampling. Approach: Using a compressed sensing subspace-based reconstruction algorithm on zero-padded k-space to estimate a higher resolution image with finer detail than current interpolation strategies. Results: Smoother diffusion encoded images and reduced spatial blurring in calculated metrics compared to standard cubic interpolation was achieved. Impact: A new method for upsampling diffusion images using subspace-based compressed sensing reconstruction is introduced that includes fine detail and reduces noise. Potential for improving on standard cubic interpolation is demonstrated, which will benefit DTI analysis including tractography.