Enhancing Precision in Diffusion MRI with multi-Bingham models of fiber crossing and dispersion
Poster No:
1297
Submission Type:
Abstract Submission
Authors:
Julio Villalón-Reina1, Charles Poirier2, Maxime Descoteaux2, Flavio Dell'Acqua3, Paul Thompson1, Rafael Neto Henriques4
Institutions:
1University of Southern California, Los Angeles, CA, 2University of Sherbrooke, Sherbrooke, Quebec, 3Kings College London, London, London, 4Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
First Author:
Co-Author(s):
Introduction:
Quantitative indicators of white matter (WM) microstructure are essential for comprehending the tissue integrity anomalies linked to neurodevelopmental, psychiatric, and neurodegenerative conditions. There is a crucial need for techniques that can separate fiber crossing and dispersion concurrently within clinically feasible scan times. In many brain disorders, affected WM fiber bundles intersect with unaffected bundles [1], making it challenging to interpret metrics derived from diffusion MRI (dMRI). Prior research has used the orientation distribution function (ODF) to generate WM microstructural metrics, such as fixel-based analysis, which concentrates on the peak amplitudes of ODF lobes [2]. An alternative approach employed a single-Bingham (SB) distribution to model individual ODF lobes, distinguishing between fiber density (FD) and dispersion [3]. We introduce a novel multi-Bingham (MB) model to simultaneously estimate FD and dispersion across all ODF lobes. Using simulated dMRI data, we show that this method enhances the accuracy of FD and dispersion estimates compared to the SB fitting and that it is sensitive to microstructural changes along particular WM bundles.
Methods:
Simulations: Using a Ball and Rackets model [5], signals were produced with an isotropic diffusivity of 0.75 µm²/ms and an axial diffusivity of 2.25 µm²/ms. The model incorporated varying numbers of rackets and concentration parameters κ1, κ2, across 30 gradient directions for each b-value of 1 and 2 ms/µm², as well as 3 b-zeros. ODFs from these synthetic signals were reconstructed with robust unbiased model-based spherical deconvolution (RUMBA) [6]. This process considered two response function kernels with specific diffusivities: kernel1=[2.25, 0, 0] µm²/ms and kernel2=[0.75, 0.75, 0.75] µm²/ms. The resulting ODFs underwent resampling on a spherical grid of 10242 vertices. SB fitting: DIPY [7] was used to fit separate Bingham functions to each ODF lobe, as proposed in [3]. MB fitting: A sum of Bingham distributions is directly fitted to the full ODF by minimizing the nonlinear objective function shown in Fig.1C, which is initialized using the parameter estimates obtained for the SB fitting. In contrast to the prior MB fitting procedure [4], the dispersion axes μ1 and μ2 are not fixed to their initial estimates, but they are treated as free model parameters. For each ODF lobe, the FD is quantified by f=d*f_k1/∑d, where d is the relative fraction computed by the integral of each fitted Bingham function and f_k1 is the volume fraction estimated from RUMBA's kernel1. Orientation dispersion indices (ODI) were quantified for each dispersion axis by ODI_i=2arctan(1/κ_i)/π.
Results:
Fig.1 shows that the SB approach's accuracy is heavily influenced by the chosen SA value; for instance, ODI_1 is underestimated with low SA values but overestimated with high SA values. In contrast, MB estimates closely match ground truths, regardless of the SA value used. Fig.2 illustrates that the effects of changing the intracellular volume fraction and ODI are indistinguishable based solely on ODF amplitudes, as lobe1's amplitude decreases in both scenarios. However, MB estimates accurately identify the decrease in bundle1's fiber density for scenario A and the increase in bundle1's ODI_1 for scenario B.
Conclusions:
Our research reveals that the poorer performance of SB fitting can be attributed to its sensitivity to the SA parameter and its dependence on sampling points from individual ODF lobes, which are susceptible to inaccuracies due to neighboring lobe influence. Through simulations of intersecting fiber bundles we demonstrated that the MB fitting technique effectively differentiated between reductions in intracellular volume fraction and increases in dispersion. Subsequent research will concentrate on assessing the MB method's clinical relevance across various disease scenarios, particularly in cases where changes in FD and dispersion serve as crucial pathological indicators.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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[4] Neto Henriques R. (2016). Mapping fibre dispersion and tract specific metrics in multiple fibre orientation using multi Bingham distributions. Proceeding of the International Society for Magnetic Resonance in Medicine; 3055
[5] Sotiropoulos S et al. (2013). Ball and Rackets: Inferring Fibre Fanning from Diffusion-weighted MRI. Neuroimage;60(2):1412-1425
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