Fast Diffusion Kurtosis Mapping of Human Brain at 7 Tesla With Hybrid Principal Component Analyses

Diffusion kurtosis has become an important magnetic resonance imaging (MRI) modality for non-invasively mapping the microstructural variations in living tissues. Theoretically, spatial resolution of diffusion (DKI) can be significantly improved by acquiring data at ultra-high fields (UHF, ≤ 7 Tesla) because increased signal-to-noise ratios. However, issues such as susceptibility artefacts and rapid signal attenuation inherent UHF-MRI have impeded adoption DKI research clinics. In this paper, we developed a new image reconstruction algorithm fast UHF. By integrating one-dimensional two-dimensional principal component analysis compressed sensing technologies, reconstruct maps from highly undersampled data. The technique was validated using randomly brain images with control database fully sampled acquisitions healthy human participants. We compared zero-filling Fourier transform similar algorithms evaluating peak ratio (PSNR) structural similarity index (SSIM) between assessing reproducibility results Bland-Altman method. found that our methods achieve least five-fold reduction acquisition time UHF high quality. Moreover, PSNR SSIM five metrics generated were superior to other when undersampling rate is echo short. proposed method valuable functional dynamic-contrast techniques Tesla or higher.