Signal-to-noise ratio-enhancing joint reconstruction for improved diffusion imaging of mouse spinal cord white matter injury

Purpose: To assess the capability of signal-to-noise ratio enhancing reconstruction (SER) to reduce the acquisition time for quantitative white matter injury assessment.

Methods: Four single-average diffusion tensor imaging (DTI) datasets were acquired for each animal from four mouse cohorts: two models of spinal cord injury and two control groups. Quantitative parameters (apparent diffusion coefficient, relative anisotropy, axial and radial diffusivities) were computed from (I) single-average data with traditional reconstruction; (II) single-average data with SER; (III) four-average data with traditional reconstruction; and (IV) single-average data with optimized multicomponent nonlocal means (OMNLM) denoising. These approaches were compared based on coefficients of variation (COVs) and whether estimated diffusion parameters were sensitive to injury.

Results: SER yielded better COVs for diffusivity and anisotropy than traditional reconstruction of single-average data, and yielded comparable COVs to that achieved with four-average data. In addition, diffusion parameters obtained using SER with single-average data had comparable injury sensitivity to those obtained from four-average data, while diffusion parameters obtained from OMNLM and traditional reconstruction of single-average data had limited sensitivity.

Conclusion: A four-fold reduction in the number of averages for quantitative diffusion imaging of small animal white matter injury is feasible using SER. Our results also underscore the need to validate nonlinear methods using task-based measures on an application-by-application basis.