Fiber direction estimation using constrained spherical deconvolution based on multi-model response function_Journal of Biomedical Engineering

Authors：

PAN Yingyu ,  WANG Yuanjun

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;

Corresponding author：

WANG Yuanjun, Email: yjusst@126.com

Keywords：

Diffusion magnetic resonance imaging; Constrained spherical deconvolution; Multi-model response function; Partial volume effect; Fiber orientation estimation

DOI：

10.7507/1001-5515.202202034

Video：

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Abstract Full text Figures/Tables Video References Cited by

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

Citation： PAN Yingyu, WANG Yuanjun. Fiber direction estimation using constrained spherical deconvolution based on multi-model response function. Journal of Biomedical Engineering, 2022, 39(6): 1117-1126. doi: 10.7507/1001-5515.202202034 Copy

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16.	Guo F H, Leemans A, Viergever M A, et al. Generalized Richardson-Lucy(GRL) for analyzing multi-shell diffusion MRI data. NeuroImage, 2020, 218: 116948.
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18.	Jha R R, Jaswal G, Bhavsar A, et al. Single-shell to multi-shell dMRI transformation using spatial and volumetric multi-level hierarchical reconstruction framework. Magn Reson Imaging, 2022, 87: 133-156.
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22.	Vaher K, Galdi P, Blesa Cabez M, et al. General factors of white matter microstructure from DTI and NODDI in the develop-ing brain. Neuroimage, 2022, 254: 119169.
23.	Zhang H, Schnerder T, Wheeler-kingshott C A, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 2012, 61(4): 1000-1016.
24.	付修威, 倪红艳. 神经突方向离散度和密度成像的原理及其在中枢神经系统的研究进展. 国际医学放射学杂志, 2020, 43(1): 68-72.
25.	De Luca A, Guo F H, Froeling M, et al. Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions(mFODs). Neuroimage, 2020, 222: 117206.
26.	Roine T, Jeurissen B, Perrone D, et al. Informed constrained spherical deconvolution(iCSD). Med Image Anal, 2015, 24(1): 269-281.
27.	岳晴, 王远军. 基于非局部约束球面反卷积模型的纤维追踪算法. 波谱学杂志, 2020, 37(4): 422-433.
28.	冯远静,吴烨,张贵军,等. 基于压缩感知高阶张量扩散磁共振稀疏成像方法. 模式识别与人工智能, 2015, 28(8): 710-719.

1. Dell’acqua F, Rizzo G, Scifo P, et al. A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging. IEEE Trans Biomed Eng, 2007, 54(3): 462-472.
2. 徐田田. 部分容积效应下的神经纤维方向估计模型与算法. 杭州: 浙江工业大学, 2017.
3. Cgappell M A, McConnell F A K, Golay X, et al. Partial volume correction in arterial spin labeling perfusion MRI: a method to disentangle anatomy from physiology or an analysis step too far?. NeuroImage, 2021, 238: 118236.
4. Glenn G R, Helpern J A, Tabesh A, et al. Quantitative assessment of diffusional kurtosis anisotropy. NMR Biomed, 2015, 28(4): 448-459.
5. Wedeen V J, Hagmann P, Tseng W Y I, et al. Mapping complex tissue architecture with diffusion spectrum magnetic reso-nance imaging. Magn Reson Med, 2005, 54(6): 1377-1386.
6. Caccialo A, Milardi D, Calamuneri A, et al. Constrained spherical deconvolution tractography reveals Cerebel-lo-Mammillary connections in humans. Cerebellum, 2017, 16(2): 483-495.
7. Dell’Acqua F, Scifo P, Rizzo G, et al. A modified damped Richardson-Lucy algorithm to reduce isotropic background ef-fects in spherical deconvolution. Neuroimage, 2010, 49(2): 1446-1458.
8. Karan P, Reymbaut A, Gilbert G, et al. Bridging the gap between constrained spherical deconvolution and diffusional var-iance decomposition via tensor-valued diffusion MRI. Med Image Anal, 2022, 79: 102476.
9. Roine T, Jeurissen B, Perrone D, et al. Isotropic non-white matter partial volume effects in constrained spherical deconvolution. Frontiers in Neuroinformatics, 2014, 8: 28.
10. Jeurissen B, Tournier J D, Dhollander T, et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 2014, 103: 411-426.
11. Jeurissen B, Szczepankiewicz F. Multi-tissue spherical deconvolution of tensor-valued diffusion MRI. NeuroImage, 2021, 245: 118717.
12. Tournier J D, Calamante F, Gadian D G, et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004, 23(3): 1176-1185.
13. Zhou Q, Michailovich O, Rayhi Y. Spatially regularized reconstruction of fibre orientation distributions in the presence of isotropic diffusion. Waterloo: University of Waterloo, 2014.
14. 冯远静, 何健忠, 李永强, 等. 神经纤维体素微结构成像估计算法研究进展. 中国科学:信息科学, 2019, 49(6): 663-684.
15. Tournier J D, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 2007, 35(4): 1459-1472.
16. Guo F H, Leemans A, Viergever M A, et al. Generalized Richardson-Lucy(GRL) for analyzing multi-shell diffusion MRI data. NeuroImage, 2020, 218: 116948.
17. Pietsch M, Christiaens D, Hutier J, et al. A framework for multicomponent analysis of diffusion MRI data over the neo-natal period. NeuroImage, 2019, 186: 321-337.
18. Jha R R, Jaswal G, Bhavsar A, et al. Single-shell to multi-shell dMRI transformation using spatial and volumetric multi-level hierarchical reconstruction framework. Magn Reson Imaging, 2022, 87: 133-156.
19. Dhollander T, Mito R, Raffelt D, et al. Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. Proc Intl Soc Mag Reson Med, 2019, 27: 0555.
20. Tax C M W, Jeurissen B, Vos S B, et al. Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data. Neuroimage, 2014, 86: 67-80.
21. Jensen J H, Helpern J A. MRI quantifification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed, 2010, 23(7): 698-710.
22. Vaher K, Galdi P, Blesa Cabez M, et al. General factors of white matter microstructure from DTI and NODDI in the develop-ing brain. Neuroimage, 2022, 254: 119169.
23. Zhang H, Schnerder T, Wheeler-kingshott C A, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 2012, 61(4): 1000-1016.
24. 付修威, 倪红艳. 神经突方向离散度和密度成像的原理及其在中枢神经系统的研究进展. 国际医学放射学杂志, 2020, 43(1): 68-72.
25. De Luca A, Guo F H, Froeling M, et al. Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions(mFODs). Neuroimage, 2020, 222: 117206.
26. Roine T, Jeurissen B, Perrone D, et al. Informed constrained spherical deconvolution(iCSD). Med Image Anal, 2015, 24(1): 269-281.
27. 岳晴, 王远军. 基于非局部约束球面反卷积模型的纤维追踪算法. 波谱学杂志, 2020, 37(4): 422-433.
28. 冯远静,吴烨,张贵军,等. 基于压缩感知高阶张量扩散磁共振稀疏成像方法. 模式识别与人工智能, 2015, 28(8): 710-719.

Journal of Biomedical Engineering

Fiber direction estimation using constrained spherical deconvolution based on multi-model response function

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