Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Di...
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Published in: | Human brain mapping Vol. 42; no. 12; pp. 3887 - 3904 |
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Main Authors: | , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Hoboken, USA
John Wiley & Sons, Inc
15-08-2021
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Subjects: | |
Online Access: | Get full text |
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Summary: | The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD‐Stream) methods, and multi‐fiber (UKF‐2T) and single‐fiber (UKF‐1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. 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 and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
In this work, we investigate the performance of four tractography methods (SD‐Stream, iFOD1, UKF‐1T, and UKF‐2T) for reconstruction of the complete retinogeniculate visual pathway (RGVP). Anatomical measurement and expert rating assessments shows that 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. However, it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. |
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Bibliography: | Funding information Jianzhong He and Fan Zhang are co‐first‐authors. National Natural Science Foundation of China, Grant/Award Numbers: 61903336, 61976190; Chinese Postdoctoral Science Foundation, Grant/Award Number: 2019M663271; Key Research & Development Project of Zhejiang Province grant, Grant/Award Number: 2020C03070; National Institutes of Health (NIH), Grant/Award Numbers: HHSN26100071, HHSN261200800001E, P41 EB015898, P41 EB015902, P41 EB028741, R01 CA235589, R01 MH074794, R01 MH111917, R01 MH119222, U01 CA199459; China Scholarship Council ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 Funding information National Natural Science Foundation of China, Grant/Award Numbers: 61903336, 61976190; Chinese Postdoctoral Science Foundation, Grant/Award Number: 2019M663271; Key Research & Development Project of Zhejiang Province grant, Grant/Award Number: 2020C03070; National Institutes of Health (NIH), Grant/Award Numbers: HHSN26100071, HHSN261200800001E, P41 EB015898, P41 EB015902, P41 EB028741, R01 CA235589, R01 MH074794, R01 MH111917, R01 MH119222, U01 CA199459; China Scholarship Council |
ISSN: | 1065-9471 1097-0193 1097-0193 |
DOI: | 10.1002/hbm.25472 |