Ensemble Tractography

Abstract

Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.

Fiber up‐sampling and quality assessment of tractograms – towards quantitative brain connectivity

“…These new fibers contribute significantly to a more optimal tractogram set. Similar effects were reported by (Takemura, Caiafa, Wandell, & Pestilli, 2016) while different algorithm parameters were introduced instead of only increasing the number of seed points. The presented framework also enables the comparison of different tractogram sets from various sources and allows a more extensive inspection of each tractogram and its strength and weaknesses without defining an explicit gold standard.…”

Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines

“…The choice of tractography method can have profound effects on the anatomical properties of the estimated WM tracts (Takemura, Caiafa, Wandell, & Pestilli, 2016). In this work, we evaluated the effect of tractography and different constraints on measures of arcuate WM laterality.…”

“…We note that LiFE (and life) is not perfect: while evaluation algorithms such as LiFE (Pestilli et al, ) or COMMIT (Daducci et al, ) constrain the tractography results and find a better solution than the initial tractogram in terms of predicting the raw dMRI data, they do not necessarily reach a unique global minimum. Multiple solutions could minimize the objective function, and these solutions would depend not only on the evaluation algorithm's implementation but also on the initial tractogram and the quality of the data (Daducci et al, ; Takemura et al, ).…”

Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET)

“…In particular, potential applications of MAGNET also include the delineation of other controversial fascicles of the human brain such as the vertical occipital fasciculus (VOT) (Yeatman et al, 2014; Takemura et al, 2015), the frontal aslant tract (FAT) (Catani et al, 2013) and the superior fronto-occipital fasciculus (sFOF) (Forkel et al, 2014; Meola et al, 2015). Like other streamline tractography methods, our magnetic-ROI approach can also be used in combination with existing streamline evaluation techniques (Smith et al, 2013; Tax et al, 2014a; Pestilli et al, 2014; Daducci et al, 2015; Takemura et al, 2016). …”