Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT)

Özarslan, Evren; Shepherd, Timothy M.; Vemuri, Baba C.; Blackband, Stephen J.; Mareci, Thomas H.

doi:10.1016/j.neuroimage.2006.01.024

Cited by 340 publications

(337 citation statements)

References 33 publications

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“…A radial truncation order n ≤ N involves (N + 1) coefficients. A low order N assumes a radial Gaussian behavior as in [26,30]; the Gaussian decay in the SPF basis arises from the normalization of the Laguerre polynomials in the spherical coordinates (more details in appendix B). On the contrary, a high order N provides a modelfree estimation.…”

Section: Spherical Polar Fourier Expansionmentioning

confidence: 99%

“…(8) (see Appendix E for more details). Additionally, the QBI [21,40,56] and the DOT [26] methods can be expressed in our approach with respectively R n (||q||) = δ(||q|| − q )/q 2 and R n (||q||) = j n (2π||q||R 0 )δ n,l where j n is the spherical Bessel function at order n, q and R 0 are two real constants (appendix C and D for more details on this). Fig.1 points out the actual adequacy of the first R n functions to the experimental MR signal from erythrocytes (appendix A for more details).…”

Section: Spherical Polar Fourier Expansionmentioning

confidence: 99%

“…The considered deconvolution kernel usually represents the signal of a single fiber model and requires a prior on either angular or radial MR signal or both. The Diffusion Orientation Transform (DOT) [26] method assumes a monoexponential radial MR decay and proposes analytical solutions to the Fourier transform using the Hankel transform. Nonetheless, the HARDI technique reduces the number of samples and the acquisition time, by restricting the acquisition to be on a single sphere.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, whereas HARDI focuses on having a lot of samples on a single sphere, HYDI focuses on a better distribution of samples on the qspace. Very few methods have been proposed so far to take advantage of this kind of sampling: the generalized DTI (GDTI) method [28,29] is based on the Fick's diffusion law, and the DOT method [26,30] has been extended to multi-exponential radial decay. Nonetheless these methods use a larger set of data and are still based on prior models of the input signal radial behavior.…”

Section: Introductionmentioning

confidence: 99%

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Efficient and robust computation of PDF features from diffusion MR signal

Assemlal

Tschumperlé

Brun

2009

Medical Image Analysis

131

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We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

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Section: Spherical Polar Fourier Expansionmentioning

confidence: 99%

Section: Spherical Polar Fourier Expansionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient and robust computation of PDF features from diffusion MR signal

Assemlal

Tschumperlé

Brun

2009

Medical Image Analysis

131

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“…HARDI methods attempt to recover more information about the angular structure of intravoxel diffusion by acquiring larger numbers of diffusion-encoding directional measurements at higher diffusionweighting factors (b values) than is routine for DTI [6][7][8]. There has been a growing list of proposed techniques for reconstructing fiber orientations from HARDI data, including spherical harmonic modeling of the apparent diffusion coefficient (ADC) profile [7,8], multitensor modeling [9], generalized tensor representations [10,11], persistent angular structure [12], circular spectrum mapping [13], q-ball imaging [14,15], spherical deconvolution [16], fiber orientation estimated on continuous axially symmetric tensors [17] and the diffusion orientation transform [18]. However, all of these HARDI approaches remain SNR-limited even on 3T scanners with parallel imaging capability, because of the need for both high spatial resolution and very strong diffusion-weighting, resulting in long examination times and/or suboptimal discrimination of intravoxel crossing fiber tracts.…”

Section: Introductionmentioning

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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High Angular Resolution Diffusion Imaging (HARDI)

Descoteaux

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article covers the young history of high angular resolution diffusion imaging (HARDI), from basic diffusion principles and diffusion tensor imaging (DTI) to q ‐space imaging, advanced modeling, and high‐order reconstruction techniques. HARDI has opened up new perspectives to noninvasively measure microstructural features and study white matter connectivity using HARDI‐based fiber tractography. HARDI is thus at the heart of modern neuroscience research and several clinical applications.

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Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT)

Cited by 340 publications

References 33 publications

Efficient and robust computation of PDF features from diffusion MR signal

Efficient and robust computation of PDF features from diffusion MR signal

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

High Angular Resolution Diffusion Imaging (HARDI)

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