Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Abstract

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

Comparison of basis functions and q‐space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging

“…Previous work furthermore demonstrated, based on a comparable imaging protocol, the suitability of CS‐DSI for various established diffusion and microstructure models such as DTI, DKI, NODDI and CHARMED compared to state‐of‐the‐art 3‐shell HARDI imaging. Also, test‐retest analysis has been conducted and the performance of distortion and motion correction has been investigated.…”

“…No in‐plane acceleration was applied (GRAPPA R=1). Sequence parameters were optimized to allow for 2.3‐fold accelerated CS‐DSI within about 12 minutes . 257 diffusion weighted and 8 interleaved, non‐weighted images were acquired with both anterior‐to‐posterior and posterior‐to‐anterior phase encoding (PE).…”

MAPL1: q‐space reconstruction using ‐regularized mean apparent propagator

“…The Cartesian data are as reported in 28 . Data come from a 3T Prisma scanner at 1.5 mm isotropic resolution (TE/TR = 105/6100 ms, , δ /Δ = 20.1/51.3 ms).…”

Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data

“…The ground truth for simulations were the inverse Fourier transform of the noisefree and fully sampled measurements, F -1 {E simulation (q)}. For the in vivo acquisition, we used fully sampled DSI acquisitions collected as in Tobisch et al 27 Data were acquired in a 3T Prisma scanner (Siemens Healthcare, Erlangen, Germany) at 1.5 mm isotropic resolution (TE/TR = 105/6100 ms, b max = 6800 s/mm 2 , ∆ = 51.3 ms, δ = 20.1 ms) using a 64-channel head-neck coil. A total of 257 diffusion weighted and eight interleaved, non-weighted images were acquired with both anterior-to-posterior and posterior-toanterior phase encoding.…”