A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging
A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging
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DOI：
10.1109/tbme.2006.888830
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发表时间：
2007-03-01
影响因子：
4.6
通讯作者：
Fazio, Ferruccio
中科院分区：
文献类型：
--
作者：
Dell'Acqua, Flavio;Rizzo, Giovanna;Fazio, Ferruccio
提出了一种解决扩散磁共振成像中光纤交叉问题的反褶积方法。为了提供信号产生过程的直接物理解释，我们从经典的多室模型开始，并根据卷积过程重写了该模型，确定了一个重要的标量参数alpha来表征物理系统响应。反褶积由理查森-露西算法的修改版本执行。仿真结果表明，即使在存在噪声数据的情况下，该方法也能正确分离光纤交叉，具有较低的信噪比，并且在反褶积过程中施加的脉冲响应函数不精确。体内数据证实了该方法在真实复杂脑结构中解决纤维交叉的有效性。这些结果表明我们的方法在纤维跟踪或连接研究中的有用性。
A deconvolution approach is presented to solve fiber crossing in diffusion magnetic resonance imaging. In order to provide a direct physical interpretation of the signal generation process, we started from the classical multicompartment model and rewrote this in terms of a convolution process, identifying a significant scalar parameter alpha to characterize the physical system response. Deconvolution is performed by a modified version of the Richardson-Lucy algorithm. Simulations show the ability of this method to correctly separate fiber crossing, even in the presence of noisy data, with lower signal-to-noise ratio, and imprecision in the impulse response function imposed during deconvolution. The in vivo data confirms the efficacy of this method to resolve fiber crossing in real complex brain structures. These results suggest the usefulness of our approach in fiber tracking or connectivity studies.