Keywords: Sparse & Low-Rank Models, 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.
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