White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction
Background. The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. To address the issue of the current classical deterministic method ten...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2024-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2024/4102461 |
| _version_ | 1827010067910623232 |
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| author | Qian Zheng Kefu Guo Yinghui Meng Jiaofen Nan Lin Xu |
| author_facet | Qian Zheng Kefu Guo Yinghui Meng Jiaofen Nan Lin Xu |
| author_sort | Qian Zheng |
| collection | DOAJ |
| description | Background. The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. To address the issue of the current classical deterministic method tending to deviate in the tracking direction in the region of crossing fiber region, in this paper, we propose an adaptive correction-based deterministic white matter fiber tracking method, named FTACTD. Methods. The proposed FTACTD method can accurately track white matter fibers by adaptively adjusting the deflection direction strategy based on the tensor matrix and the input fiber direction of adjacent voxels. The degree of correction direction changes adaptively according to the shape of the diffusion tensor, mimicking the actual tracking deflection angle and direction. Furthermore, both forward and reverse tracking techniques are employed to track the entire fiber. The effectiveness of the proposed method is validated and quantified using both simulated and real brain datasets. Various indicators such as invalid bundles (IB), valid bundles (VB), invalid connections (IC), no connections (NC), and valid connections (VC) are utilized to assess the performance of the proposed method on simulated data and real diffusion-weighted imaging (DWI) data. Results. The experimental results of the simulated data show that the FTACTD method tracks outperform existing methods, achieving the highest number of VB with a total of 13 bundles. Additionally, it identifies the least number of incorrect fiber bundles, with only 32 bundles identified as wrong. Compared to the FACT method, the FTACTD method reduces the number of NC by 36.38%. In terms of VC, the FTACTD method surpasses even the best performing SD_Stream method among deterministic methods by 1.64%. Extensive in vivo experiments demonstrate the superiority of the proposed method in terms of tracking more accurate and complete fiber paths, resulting in improved continuity. Conclusion. The FTACTD method proposed in this study indicates superior tracking results and provides a methodological basis for the investigating, diagnosis, and treatment of brain disorders associated with white matter fiber deficits and abnormalities. |
| first_indexed | 2024-03-08T03:13:55Z |
| format | Article |
| id | doaj.art-92817eca73dc4d45a2e760074f89c881 |
| institution | Directory Open Access Journal |
| issn | 1687-4196 |
| language | English |
| last_indexed | 2025-02-18T12:59:21Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj.art-92817eca73dc4d45a2e760074f89c8812024-11-02T03:56:31ZengHindawi LimitedInternational Journal of Biomedical Imaging1687-41962024-01-01202410.1155/2024/4102461White Matter Fiber Tracking Method with Adaptive Correction of Tracking DirectionQian Zheng0Kefu Guo1Yinghui Meng2Jiaofen Nan3Lin Xu4Zhengzhou University of Light IndustryZhengzhou University of Light IndustryZhengzhou University of Light IndustryZhengzhou University of Light IndustryChengdu University of Traditional Chinese MedicineBackground. The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. To address the issue of the current classical deterministic method tending to deviate in the tracking direction in the region of crossing fiber region, in this paper, we propose an adaptive correction-based deterministic white matter fiber tracking method, named FTACTD. Methods. The proposed FTACTD method can accurately track white matter fibers by adaptively adjusting the deflection direction strategy based on the tensor matrix and the input fiber direction of adjacent voxels. The degree of correction direction changes adaptively according to the shape of the diffusion tensor, mimicking the actual tracking deflection angle and direction. Furthermore, both forward and reverse tracking techniques are employed to track the entire fiber. The effectiveness of the proposed method is validated and quantified using both simulated and real brain datasets. Various indicators such as invalid bundles (IB), valid bundles (VB), invalid connections (IC), no connections (NC), and valid connections (VC) are utilized to assess the performance of the proposed method on simulated data and real diffusion-weighted imaging (DWI) data. Results. The experimental results of the simulated data show that the FTACTD method tracks outperform existing methods, achieving the highest number of VB with a total of 13 bundles. Additionally, it identifies the least number of incorrect fiber bundles, with only 32 bundles identified as wrong. Compared to the FACT method, the FTACTD method reduces the number of NC by 36.38%. In terms of VC, the FTACTD method surpasses even the best performing SD_Stream method among deterministic methods by 1.64%. Extensive in vivo experiments demonstrate the superiority of the proposed method in terms of tracking more accurate and complete fiber paths, resulting in improved continuity. Conclusion. The FTACTD method proposed in this study indicates superior tracking results and provides a methodological basis for the investigating, diagnosis, and treatment of brain disorders associated with white matter fiber deficits and abnormalities.http://dx.doi.org/10.1155/2024/4102461 |
| spellingShingle | Qian Zheng Kefu Guo Yinghui Meng Jiaofen Nan Lin Xu White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction International Journal of Biomedical Imaging |
| title | White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction |
| title_full | White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction |
| title_fullStr | White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction |
| title_full_unstemmed | White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction |
| title_short | White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction |
| title_sort | white matter fiber tracking method with adaptive correction of tracking direction |
| url | http://dx.doi.org/10.1155/2024/4102461 |
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