Hybrid diffusion imaging

Abstract

Diffusion measurements in the human central nervous system are complex to characterize and a broad spectrum of methods have been proposed. In this study, a comprehensive diffusion encoding and analysis approach, Hybrid Diffusion Imaging (HYDI), is described. The HYDI encoding scheme is composed of multiple concentric "shells" of constant diffusion-weighting, which may be used to characterize the signal behavior with low, moderate and high diffusion-weighting. HYDI facilitates the application of multiple data-analyses strategies including diffusion tensor imaging (DTI), multiexponential diffusion measurements, diffusion spectrum imaging (DSI) and q-ball imaging (QBI). These different analysis strategies may provide complementary information. DTI measures (mean diffusivity and fractional anisotropy) may be estimated from either data in the inner shells or the entire HYDI data. Fast and slow diffusivities were estimated using a nonlinear least-squares biexponential fit on geometric means of the HYDI shells. DSI measurements from the entire HYDI data yield empirical model-independent diffusion information and are well-suited for characterizing tissue regions with complex diffusion behavior. DSI measurements were characterized using the zero displacement probability and the mean squared displacement. The outermost HYDI shell was analyzed using QBI analysis to estimate the orientation distribution function (ODF), which is useful for characterizing the directions of multiple fiber groups within a voxel. In this study, a HYDI encoding scheme with 102 diffusion-weighted measurements was obtained over most of the human cerebrum in under 30 minutes.

Diffeomorphic Metric Mapping of Hybrid Diffusion Imaging Based on BFOR Signal Basis

“…Seven HYDI datasets used in this study consisted of 6 shells corresponding to b-values of 0, 300, 1200, 2700, 4800, and 7500 s/mm 2 . We refer readers to [15] for more details on the HYDI acquisition. In our experiments, we represented HYDI DW signals using the BFOR signal basis with upto the fourth order modified SH bases and upto the sixth order spherical Bessel function.…”

“…MDWI can characterize more complex neural fiber geometries when compared to single b-value techniques like diffusion tensor imaging (DTI) or high angular resolution diffusion imaging (HARDI). Hybrid diffusion imaging (HYDI) [15] is a mDWI technique that samples the diffusion signal along concentric spherical shells in q-space, with the number of encoding directions increased with each shell to increase the angular resolution with the level of diffusion weighting. Originally, HYDI employed the fast Fourier transform (FFT) to reconstruct the EAP.…”

Efficient and robust computation of PDF features from diffusion MR signal

“…(15) simply turns into a very fast dot product between two vectors of SPF coefficients. YuChien et al proposed a similar approach in [27] where some specific features of the PDF are computed by a projection in the q-space between a feature function h and diffusion signal E. However, the latter method is not based on a continuous representation of the signal, and numerical issues coming from the computation of the integral q E(q)h(q)dq with a discrete sampling arise. Note that with our settings, the QBI and the DOT methods analytically express themselves as specific features of the PDF, respectively the FRT and the iso-radius.…”

“…Multi-sphere HARDI: In order to overcome the problems of the long acquisition time (as in DSI) and the missing radial or angular sampling (as in HARDI and q-space NMR), Hybrid Diffusion Imaging (HYDI) has been recently introduced by Wu et al in [27]. It proposes to extend the acquisition to multiple spheres in the q-space.…”

Development and initial evaluation of 7-T q-ball imaging of the human brain

“…Indeed, the low SNR of HARDI at 3T has motivated the introduction of different forms of regularization to improve the reliability of fiber orientation determination [19][20][21]. Proposed methods for more fully sampling q-space often require even higher diffusion-weighting factors than is typical for HARDI [22][23][24]; hence, these techniques might derive even greater benefits from better SNR.…”