Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques

Campbell, Jennifer S. W.; Siddiqi, Kaleem; Rymar, Vladimir V.; Sadikot, Abbas F.; Pike, G. Bruce

doi:10.1016/j.neuroimage.2005.05.014

Cited by 153 publications

(135 citation statements)

References 32 publications

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“…We generated 100 uniform tensor orientations over one hemisphere using an analytical approach (Hasan et al, 2001, Wong andRoos, 1994). The angular error was defined as the angular differences between the ideal direction and the direction of the maximum likelihood of the ODF profile which is obtained from either the QBI (outermost shell) or DSI (all shells) analyses (Campbell et al, 2004(Campbell et al, , 2005. For the investigation of the ODF angular resolution, a WM model of two-crossing fibers (i.e.…”

Section: Computer Simulationmentioning

confidence: 99%

Hybrid diffusion imaging

2007

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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.

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Section: Computer Simulationmentioning

confidence: 99%

Hybrid diffusion imaging

2007

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“…While the two methods differ significantly in their approach to recovering angular information from the measured diffusion data, both define a continuous function over the sphere that encodes the anisotropy of diffusion within each voxel. The correspondence between peaks of this function and multimodal fiber orientations has been established experimentally for QBI using phantom models (27,28).Although it was originally formulated using spherical radial basis functions (RBFs), the q-ball method can be implemented using a number of well-characterized spherical basis sets, each of which yields a reconstruction with different properties. In this paper we describe an implementation of QBI using spherical harmonics, a basis set that has been widely applied to various scientific applica-…”

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confidence: 99%

Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis

Hess

Mukherjee

Han

et al. 2006

Magnetic Resonance in Med

330

368

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The past decade has seen rapid advances in the development of diffusion MRI techniques that permit for the first time the noninvasive characterization of neural architecture. While the exact biophysical determinants of the diffusion signal have yet to be completely elucidated, it is now generally accepted that microscopic boundaries to diffusion in the brain coincide with the local orientations of white matter (WM) fiber tracts (1). The advent of diffusion tensor imaging (DTI) as a tool for modeling intravoxel diffusion has inspired a number of promising applications in which WM connectivity can be evaluated in both health and disease (2,3). Measures derived from the tensor are now widely used to characterize regional anisotropy and orientation of WM throughout the brain, and more recently have been incorporated into tractography algorithms to allow 3D delineation of fiber pathways (4 -6).A major shortcoming of DTI lies in its inability to accurately characterize diffusion in complex WM, where fiber tracts with different orientations intersect or are otherwise partial volume averaged within a voxel (7-9). This limitation presents a significant obstacle to routine clinical application of DTI. For example, in brain tumor patients, presurgical DTI tractography fails to delineate the course of the pyramidal tract in regions of fiber crossing (10). Although intravoxel diffusion in these areas of complex WM can be more accurately characterized using the qspace formalism (11), long experiment times and heavy gradient demands preclude routine in vivo application of this technique in humans. High angular resolution diffusion imaging (HARDI) represents an alternative approach that strives to improve imaging efficiency by recovering the angular structure of diffusion in lieu of the 3D spindisplacement probability function. Shorter imaging times make HARDI particularly promising for clinical applications, especially when combined with the greater signalto-noise ratios (SNRs) afforded by high-field MR systems (12) and the reduction in single-shot echo-planar image artifacts provided by parallel imaging (12)(13)(14).Several approaches for reconstructing fiber orientations from HARDI data have been proposed, including spherical harmonic modeling of the apparent diffusion coefficient (ADC) profile (15,16), multitensor modeling (17), generalized tensor representations (18,19), circular spectrum mapping (20), q-ball imaging (QBI) (21-24), persistent angular structure (25), and spherical deconvolution (26). Among these techniques, QBI and spherical deconvolution have generated considerable interest due to their linearity and sensitivity to multimodal diffusion. QBI has the additional desirable property of model independence because it does not require ad hoc measurement of a "response function" in order to reconstruct fiber orientations. While the two methods differ significantly in their approach to recovering angular information from the measured diffusion data, both define a continuous function over the sphere that encodes the ...

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“…Of these HARDI methods, q-ball imaging in particular has proven robust in studies by multiple groups [14,15,17,19] and has been validated with both simulations and phantom experiments [29,30]. For visualizing the complex white matter anatomy of the human brain in vivo, high spatial resolution, high angular resolution, and high SNR of the diffusion-weighted data need to be achieved simultaneously.…”

Section: Discussionmentioning

confidence: 99%

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

Mukherjee

Hess

et al. 2008

Magnetic Resonance Imaging

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Diffusion tensor imaging (DTI) noninvasively depicts white matter connectivity in regions where the Gaussian model of diffusion is valid, but yields inaccurate results where diffusion has a more complex distribution, such as fiber crossings. Q-ball imaging (QBI) overcomes this limitation of DTI by more fully characterizing the angular dependence of intravoxel diffusion with larger numbers of diffusion-encoding directional measurements at higher diffusion-weighting factors (b values). However, the former results in longer acquisition times and the latter results in lower signal-to-noise ratio (SNR). In this project, we developed specialized 7 Tesla acquisition methods utilizing novel radiofrequency pulses, 8-channel parallel imaging EPI, and high-order shimming with a phase-sensitive multichannel B 0 field map reconstruction. These methods were applied in initial healthy adult volunteer studies which demonstrated the feasibility of performing 7T QBI. Preliminary comparisons of 3T with 7T within supratentorial crossing white matter tracts document a 79.5% SNR increase for b=3000 s/mm 2 (p=0.0001), and a 38.6% SNR increase for b=6000 s/mm 2 (p=0.015). Using spherical harmonic reconstruction of the q-ball orientation distribution function at b=3000 s/mm 2 , 7T QBI allowed accurate visualization of crossing fiber tracts with fewer diffusion-encoding acquisitions than at 3T. The improvement of 7T QBI at b factors as high as 6000 s/mm 2 resulted in better angular resolution than 3T for depicting fibers crossing at shallow angles. Although the increased susceptibility effects at 7T caused problematic distortions near brain-air interfaces at the skull base and posterior fossa, these initial 7T QBI studies demonstrated excellent quality in much of the supratentorial brain with significant improvements as compared to 3T acquisitions in the same individuals.

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Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques

Cited by 153 publications

References 32 publications

Hybrid diffusion imaging

Hybrid diffusion imaging

Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis

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

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