Biology subjects

Zhylka, A.

Publications and source records attributed to Zhylka, A..

2 recordsLinked to original sources

bioRxiv2021

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

neuroscience↗

bioRxiv2020

Anatomically informed multi-level fiber tractography

Diffusion weighted MR imaging can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways. A common task is the delineation of the corticospinal tract in its full extent because lesions to this bundle can severely affect the quality of life. However, this is challenging as existing tractography algorithms typically produce either incomplete results or multiple false-positive tracts. In this work, we suggest a novel approach to fiber tractography that reconstructs multi-level structures by progressively taking into account previously unused fiber orientations. Anatomical priors are used in order to minimize the number of false-positive pathways. The devised method was evaluated on synthetic data with different noise levels. Additionally, it was tested on in-vivo data by reconstructing the corticospinal tract and it was compared to conventional deterministic and probabilistic approaches. The corticospinal tract reconstructed by our method includes lateral projections that could not be observed with deterministic methods, while avoiding spurious tracts reconstructed by probabilistic tractography. Furthermore, the proposed algorithm preserves the neuroanatomical topology of the pathways to a larger extent as compared to probabilistic tractography.

neuroscience↗