It has been suggested that, high b-value diffusion weighted MRI improves the sensitivity and specificity of these images to tissue microstructure when compared with "clinical" b-value diffusion weighted MRI (b ≈ 1000 s/mm(2)). However, it suffers from poor signal to noise ratio - leading to longer acquisition times and therefore more motion artifacts. Together with the orientational sensitivity of the diffusion weighted MRI signal, the contrast at different b-values and different gradient directions is significantly different. These features of high b-value diffusion images preclude the ability to perform conventional image-registration-based motion/distortion correction. Here, we suggest a framework based on both experimental data (diffusion tensor MRI) and simulations (using the composite hindered and restricted model of diffusion framework) to correct the motion induced misalignments and artifacts of high b-value diffusion weighted MRI. This approach was evaluated using visual assessment of the registered diffusion weighted MRI and the composite hindered and restricted model of diffusion analysis results, as well as residual analysis to assess the quality of the composite hindered and restricted model of diffusion fitting. Both qualitative and quantitative results demonstrate an improvement in fitting the data to the composite hindered and restricted model of diffusion model following the suggested registration framework, thereby, addressing a long-standing problem and making the correction of motion/distortions in data collected at high b-values feasible for the first time.
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