A Simple and Efficient Method for Acceleration and Denoising of Multi-Contrast Diffusion Data: Application to Q-Space and HARDI

Ana-Maria Oros-Peusquens¹, Nadim Jon Shah¹, ²

We present a method combining acceleration of multi-contrast diffusion data using UNFOLD with denoising based on singular value decomposition (SVD). UNFOLD is ideally suited for acceleration of q-space acquisition because q-space information is extracted from the Fourier transform of the q-dependent diffusion signal. The information is largely contained in the q-space centre, a region unaffected by aliasing at moderate UNFOLD acceleration (2-4). Multi-direction HARDI signal also has Fourier components in a restricted interval and is thus very well suited for UNFOLD acceleration. SVD denoising works extremely well for q-space data and also, but less spectacularly, for quantitative information obtained with HARDI. In conclusion, acceleration and denoising of q-space and HARDI data is demonstrated, using simple methods with numerically undemanding reconstruction. The same method should work well for combinations of q-space and HARDI, such as diffusion spectrum imaging (DSI).