Diffusiontensor magnetic resonance imaging is widely used to study the structure of the fiber pathways of brain white matter. However, the diffusiontensor cannot capture complex intravoxel fiber architecture such as fiber crossings of bifurcations. Consequently, a number of methods have been proposed to recover intravoxel fiber bundle orientations from high angular resolutiondiffusionimaging scans, optimized to resolve fiber crossings. It is important to improve the brain tractography by applying these multifiber methods to diffusiontensor protocols with a clinicalb-value (low), which are optimized on computing tensor scalar statistics. In order to characterize the variance among different methods, consequently to be able to select the most appropriate one for a particular application, it is desirable to compare them under identical experimental conditions.Methods:
In this work, the authors study how QBall, spherical deconvolution, persistent angular structure, stick and ball, diffusion basis functions, and analytical QBall methods perform under clinically-realistic scanning conditions, where theb-value is typically lower (around 1000 s/mm2), and the number of diffusion encoding orientations is fewer (30–60) than in dedicated high angular resolutiondiffusionimaging scans. To characterize the performance of the methods, they consider the accuracy of the estimated number of fibers, the relative contribution of each fiber population to the total magnetic resonance signal, and the recovered orientation error for each fiber bundle. To this aim, they use four different sources of data: synthetic data from Gaussian mixture model, cylinder restricted model, and in vivo data from two different acquisition schemes.Results:
Results of their experiments indicate that: (a) it is feasible to apply only a subset of these methods to clinical data sets and (b) it allows one to characterize the performance of each method. In particular, two methods are not feasible to the kind of magnetic resonance diffusion data they test. By the characterization of their systematic behavior, among other conclusions, they report the method which better performs for the estimation of the number of diffusion peaks per voxel, also the method which better estimates the diffusion orientation.Conclusions:
The framework they propose for comparison allows one to effectively characterize and compare the performance of the most frequently used multifiber algorithms under realistic medical settings and realistic signal–to–noise ratio environments. The framework is based on several crossings with a non–orientational bias and different signal models. The results they present are relevant for medical doctors and researchers, interested in the use of the multifiber solution for tractography.
The authors thank Maxime Descoteaux, Rachid Deriche and Donald Tournier for the provision of code, they also thank Kiran Seunarine for help with the Camino toolkit. A. Ramirez-Manzanares was partially supported by a SNI Scholarship from The National Council for Science and Technology (CONACYT), Mexico.
II. EXPERIMENTAL METHODS
II.A. Comparison framework
Data & Media loading...
Article metrics loading...
Full text loading...