Regularized, fast, and robust analytical Q‐ball imaging

Descoteaux, Maxime; Angelino, Elaine; Fitzgibbons, Shaun; Deriche, Rachid

doi:10.1002/mrm.21277

Cited by 727 publications

(922 citation statements)

References 32 publications

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“…In every voxel of the dataset, the diffusion signal corresponding to the underlying fiber configuration is simulated according to the same gradient list as our real DSI data. The diffusion signal, S(q), is simulated using the classical Gaussian mixture model (Tuch, 2004;Descoteaux et al, 2007;Canales-Rodríguez et al, 2009):…”

Section: Synthetic Data Simulationmentioning

confidence: 99%

“…To compute the DNC and the AE, we extract the maxima on a discrete grid with N = 4000 uniform points (Descoteaux et al, 2007) for the estimated ODFs and compare them to the ground truth maxima. Then, the DNC becomes the mean difference between the number of maxima extracted on the estimated ODFs and the true number of maxima, and the AE is computed between the maxima extracted on the estimated ODFs and the respective maxima within the ground truth.…”

Section: Synthetic Data Simulationmentioning

confidence: 99%

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Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Gramfort¹,

Poupon²,

Descoteaux³

2014

Medical Image Analysis

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To cite this version:Alexandre Gramfort, Cyril Poupon, Maxime Descoteaux. Denoising and fast diffusion imaging with physically constrained sparse dictionary learning. Medical Image Analysis, Elsevier, 2013, 18 (1) Abstract Diffusion-weighted imaging (DWI) allows imaging the geometry of water diffusion in biological tissues. However, DW images are noisy at high b-values and acquisitions are slow when using a large number of measurements, such as in Diffusion Spectrum Imaging (DSI). This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality. To capture the structure of DWI data, we use sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity. The method learns a dictionary of diffusion profiles on all the DW images at the same time and then scales to full brain data. Its performance is investigated with simulations and two real DSI datasets. We obtain better signal estimates from noisy measurements than by applying mirror symmetry through the q-space origin, Gaussian denoising or state-ofthe-art non-local means denoising. Using a high-resolution dictionary learnt on another subject, we show that we can reduce the number of images acquired while still generating high resolution DSI data. Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible with approximately 40 measurements. This opens important perspectives for the connectomics community using DSI.

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Section: Synthetic Data Simulationmentioning

confidence: 99%

Section: Synthetic Data Simulationmentioning

confidence: 99%

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Gramfort¹,

Poupon²,

Descoteaux³

2014

Medical Image Analysis

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show abstract

“…(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%

“…Fig.12c). Whereas cerebrospinal fluid area are ex-(a) S 0 (b) DTI [5] (c) QBI [56] (d) G=ODF [74] (e) Rice (f) Soft Reg. pected to exhibits isotropic diffusion, the ODF obtained by the QBI method exhibits anisotropy.…”

Section: In-vivo Experimentsmentioning

confidence: 99%

“…(a) DTI [5] (b) QBI [56] (c) Our method, G=ODF We expand the previous expression into a series made of spherical harmonics y lm and radial functions R n (q) = q −2 δ(q − q), δ(u T · k)δ(q − q)R n (q)y lm (u)dq = 2πP l (0)y lm (k) (Funk-Hecke theorem [40,56]) (38) Note that Tuch demonstrated in [21] that the FRT of the signal E is expressed in the displacement space as:…”

Section: A First Radial R Nmentioning

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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A Multicenter Tractography Study of Deep White Matter Tracts in Bipolar I Disorder

et al. 2014

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IMPORTANCE Tractography studies investigating white matter (WM) abnormalities in patients with bipolar disorder have yielded heterogeneous results owing to small sample sizes. The small size limits their generalizability, a critical issue for neuroimaging studies of biomarkers of bipolar I disorder (BPI). OBJECTIVES To study WM abnormalities using whole-brain tractography in a large international multicenter sample of BPI patients and to compare these alterations between patients with or without a history of psychotic features during mood episodes. DESIGN, SETTING, AND PARTICIPANTS A cross-sectional, multicenter, international, Q-ball imaging tractography study comparing 118 BPI patients and 86 healthy control individuals. In addition, among the patient group, we compared those with and without a history of psychotic features. University hospitals in France, Germany, and the United States contributed participants. INTERVENTIONS Participants underwent assessment using the Diagnostic Interview for Genetic Studies at the French sites or the Structured Clinical Interview for DSM-IV at the German and US sites. Diffusion-weighted magnetic resonance images were acquired using the same acquisition parameters and scanning hardware at each site. We reconstructed 22 known deep WM tracts using Q-ball imaging tractography and an automatized segmentation technique. MAIN OUTCOMES AND MEASURES Generalized fractional anisotropy values along each reconstructed WM tract. RESULTS Compared with controls, BPI patients had significant reductions in mean generalized fractional anisotropy values along the body and the splenium of the corpus callosum, the left cingulum, and the anterior part of the left arcuate fasciculus when controlling for age, sex, and acquisition site (corrected for multiple testing). Patients with a history of psychotic features had a lower mean generalized fractional anisotropy value than those without along the body of the corpus callosum (corrected for multiple testing). CONCLUSIONS AND RELEVANCE In this multicenter sample, BPI patients had reduced WM integrity in interhemispheric, limbic, and arcuate WM tracts. Interhemispheric pathways are more disrupted in patients with than in those without psychotic symptoms. Together these results highlight the existence of an anatomic disconnectivity in BPI and further underscore a role for interhemispheric disconnectivity in the pathophysiological features of psychosis in BPI.

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Regularized, fast, and robust analytical Q‐ball imaging

Cited by 727 publications

References 32 publications

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Efficient and robust computation of PDF features from diffusion MR signal

A Multicenter Tractography Study of Deep White Matter Tracts in Bipolar I Disorder

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