Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

Abstract

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. Anatomically, the RGVP can be separated into four subdivisions, including two decussating and two non-decussating fiber pathways, which cannot be identified by conventional magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the anatomy of the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for tractographic reconstruction of the RGVP. In this study, four different tractography algorithms, including constrained spherical deconvolution (CSD) model based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) tractography methods, are compared for reconstruction of the RGVP. Experiments are performed using diffusion MRI data of 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Anatomical measurements are used to assess the advantages and limitations of the four tracking strategies, including the reconstruction rate of the four RGVP subdivisions, the percentage of decussating fibers, the correlation between volumes of the traced RGVPs and a T1w-based RGVP segmentation, and an expert judgment to rank the anatomical appearance of the reconstructed RGVPs. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy.

Automatic oculomotor nerve identification based on data‐driven fiber clustering

“…Tractography methods are widely used in cranial nerve studies (Behan et al, 2017; Castellaro et al, 2020; He et al, 2021; Hodaie et al, 2010). To find a better method for OCN reconstruction, we compare the performance of three different direction estimation methods including constraint spherical deconvolution (CSD; Behan et al, 2017; Tournier, Calamante, & Connelly, 2012), multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD), and DTI (Hodaie et al, 2010) to reconstruct the 3D trajectory of OCN.…”

Predictive Radiation Oncology – A New NCI–DOE Scientific Space and Community

“…In radiation oncology today, imaging information is used ubiquitously across the care pathway, including initial assessment and staging, decision making to treat a patient with radiotherapy, radiation treatment planning and delivery, and treatment response assessment. Advances in imaging have introduced novel approaches to characterizing tissues with greater complexity (44,45). However, our current use of imaging data, which largely relies on qualitative or semiquantitative, manual analysis only scratches the surface of the full potential of quantitative imaging for personalized treatment of cancer patients.…”

“…However, our current use of imaging data, which largely relies on qualitative or semiquantitative, manual analysis only scratches the surface of the full potential of quantitative imaging for personalized treatment of cancer patients. In parallel, advances in molecular biomarkers, measured in tumor tissues or biological fluids, are increasing our ability to predict radiation responses and measure them during treatment (44)(45)(46). Some of the major challenges in clinical translation of these novel imaging approaches and biomarkers include, first, better understanding of the underlying biological mechanisms that are being revealed through these imaging measures to allow for actionability and, second, the need for tools and approaches to enable consistent, quantitative imaging measurements within broad clinical settings.…”