A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk–Radon transform

Tristán‐Vega, Antonio; Westin, Carl‐Fredrik; Aja‐Fernández, Santiago

doi:10.1016/j.neuroimage.2009.09.070

Cited by 41 publications

(54 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite this threshold, false positive peaks were still prevalent in our data when fitting to high SH orders, even in regions known to contain a single dominant fiber population. In addition to regularization parameters, there are also multiple variations of the q-ball algorithm itself, some of which have been shown to result in sharper ODFs (45–47). Furthermore, QBI is just one of a large number of HARDI techniques (see (48) and (49) for validation and performance assessment of a comprehensive set of algorithms on phantom and simulated data).…”

Section: Discussionmentioning

confidence: 99%

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Schilling

Nath

Blaber

et al. 2017

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used to study brain architecture in vivo. Simulation and phantom-based studies suggest that QBI results are affected by the b-value, the number of diffusion weighting directions, and the signal-to-noise ratio (SNR). However, optimal acquisition schemes for QBI in clinical settings are largely undetermined given empirical (observed) imaging considerations. In this study, we acquire a HARDI dataset at five b-values with 11 repetitions on a single subject to investigate the effects of acquisition scheme and subsequent analysis models on the accuracy and precision of measures of tissue composition and fiber orientation derived from clinically feasible QBI at 3T. Clinical feasibility entails short scan protocols - less than five minutes in the current study - resulting in lower SNR, lower b-values, and fewer diffusion directions than are typical in most QBI protocols with research applications, where time constraints are less prevalent. In agreement with previous studies, we find that the b-value and number of diffusion directions impact the magnitude and variation of QBI indices in both white matter and gray matter regions; however, QBI indices are most heavily dependent on the maximum order of the spherical harmonic (SH) series used to represent the diffusion orientation distribution function (ODF). Specifically, to ensure numerical stability and reduce the occurrence of false peaks and inflated anisotropy, we recommend oversampling by at least 8–12 more diffusion directions than the number of estimated coefficients for a given SH order. In addition, in an equal scan time comparison of QBI accuracy, we find that increasing the directional resolution of the HARDI dataset is preferable to repeating observations; however, our results indicate that as few as 32 directions at a low b-value (1,000 s/mm2) captures most of the angular information in the q-ball ODF. Our findings provide guidance for determining an optimal acquisition scheme for QBI in the low SNR and low scan time regime, and suggest that care must be taken when choosing the basis functions used to represent the QBI ODF.

show abstract

Section: Discussionmentioning

confidence: 99%

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Schilling

Nath

Blaber

et al. 2017

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…This feature is shared by several of the most popular HARDI approaches, so a similar analytical study could be performed for Q-Balls (Tuch, 2004) or OPDT-like estimators (Tristán-Vega et al, 2010) by means of the spectral characterization of such matrices. The impact of Rician bias has been otherwise pointed out empirically by Clarke et al (2008), so, in light of the present work, it is expected that some important distortions shall be driven by nc- χ signals.…”

Section: Discussionmentioning

confidence: 99%

Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals

2012

Self Cite

View full text Add to dashboard Cite

Least Squares (LS) and its minimum variance counterpart, Weighted Least Squares (WLS), have become very popular when estimating the Diffusion Tensor (DT), to the point that they are the standard in most of the existing software for diffusion MRI. They are based on the linearization of the Stejskal-Tanner equation by means of the logarithmic compression of the diffusion signal. Due to the Rician nature of noise in traditional systems, a certain bias in the estimation is known to exist. This artifact has been made patent through some experimental set-ups, but it is not clear how the distortion translates in the reconstructed DT, and how important it is when compared to the other source of error contributing to the Mean Squared Error (MSE) in the estimate, i.e. the variance. In this paper we propose the analytical characterization of log-Rician noise and its propagation to the components of the DT through power series expansions. We conclude that even in highly noisy scenarios the bias for log-Rician signals remains moderate when compared to the corresponding variance. Yet, with the advent of Parallel Imaging (pMRI), the Rician model is not always valid. We make our analysis extensive to a number of modern acquisition techniques through the study of a more general Non Central-Chi (nc-χ) model. Since WLS techniques were initially designed bearing in mind Rician noise, it is not clear wether or not they still apply to pMRI. An important finding in our work is that the common implementation of WLS is nearly optimal when nc-χ noise is considered. Unfortunately, the bias in the estimation becomes far more important in this case, to the point that it may nearly overwhelm the variance in given situations. Furthermore, we evidence that such bias cannot be removed by increasing the number of acquired gradient directions. A number of experiments have been conducted that corroborate our analytical findings, while in vivo data have been used to test the actual relevance of the bias in the estimation.

show abstract

“…Then, the field of constant solid angle ODFs (csa-ODF) was estimated using spherical harmonics (SH) order 4 and lambda regularization of 0.006 (Aganj et al, 2010;Tristan-Vega et al, 2010). From this csa-ODF field, the generalized FA (GFA) was computed and compared with gold standard using RMSE.…”

Section: Comparison Of Tensor Estimation a N D Orientation Distributimentioning

confidence: 99%

Collaborative patch-based super-resolution for diffusion-weighted images

et al. 2013

View full text Add to dashboard Cite

To cite this version:Pierrick Coupé, José V Manjón, Maxime Chamberland, Maxime Descoteaux, Bassem Hiba. Collaborative patch-based super-resolution for diffusion-weighted images.. NeuroImage, Elsevier, 2013 AbstractIn this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non diffusion image (b0) t o c o n s t r a i n t h e r e c o n s t r u c t i o n o f DW i m a g e s .An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms o f improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images a r e p r e s e n t e d a t 0 . 6 x 0 . 6 x 0 . 6 m m 3 a n d 0.4x0.4x0.4mm 3 using our gold standard b a s e d o n the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach t o a c c u r a t e l y analyze white matter brain architecture.

show abstract

A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk–Radon transform

Cited by 41 publications

References 31 publications

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals

Collaborative patch-based super-resolution for diffusion-weighted images

Contact Info

Product

Resources

About