基于非凸正则球面反卷积的体素内纤维走向分布估计

Estimation of Intravoxel Fiber-Orientation Distribution via Non-Convex Regularized Spherical Deconvolution

针对经典的基于球面反卷积的体素内纤维走向分布估计方法对噪声非常敏感的问题,提出一种非凸正则球面反卷积方法。该方法基于邻域体素间纤维走向分布的相似性构造非凸空间正则项,采用改进的Richardson-Lucy算法求解非凸正则反卷积问题。基于二张量数据和HARDI模型数据进行仿真,结果表明,与经典的球面反卷积方法和全变分正则化球面反卷积方法相比,所提出的方法估计结果的平均角度误差分别降低了约52%和9%,在抑制噪声的同时能够保持体素内纤维走向的细节信息。

Since the spherical-deconvolution(SD)-based intravoxel fiber-orientation distribution(FOD)estimation method is highly sensitive to noise,a non-convex regularized SD method is proposed.It constructs a non-convex spatial regularization based on the FOD similarity between neighboring voxels and resolves the non-convex regularized SD problem using the modified Richardson-Lucy algorithm.The simulated results based on data in two tensors model and HARDI(high-angular-resolution diffusion imaging)model show that,compared with the conventional SD and total-variation regularized SD methods,the proposed method generates FODs with a lower mean angular error(reduced by 52%and 9%,respectively)and exhibits better noise immunity and detail preservation of the fiber orientations.