Miao Peng, Li Nan, Thakor Nitish V, Tong Shanbao
Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P.R. China.
Opt Express. 2010 Jan 4;18(1):218-36. doi: 10.1364/OE.18.000218.
In this paper, we develop a random process theory to explain the laser speckle phenomena. The relation between the probability distribution of speckle's integrated intensity random process Y(t) and the relative velocity v(t) is derived. Based on the random process theory, traditional spatial or temporal laser speckle contrast analysis (i.e. spatial or temporal LASCA) can be derived as the spatial or temporal estimators respectively. Both spatial LASCA and temporal LASCA suffer from noise due to insufficient statistics and nonstationarity in either spatial or temporal domain. Furthermore, either LASCA results in a reduction of spatial or temporal resolution. A new random process estimator is proposed and able to overcome these drawbacks. In an in-vitro study, random process estimator outperforms either spatial LASCA or temporal LASCA by providing much higher SNR (random process estimator vs. spatial LASCA vs. temporal LASCA: 33.64+/-6.87 (mean+/-s.t.d.) vs. 9.08+/-2.85 vs. 3.83+/-1.05). In an in-vivo structural imaging study, random process estimator efficiently suppresses the noise in contrast image and thus improves the distinguishability of small vessels. In a functional imaging study of cerebral blood flow change in the somatosensory cortex induced by rat's hind paw stimulation, random process estimator provides much lower estimation errors in single trial data (random process estimator vs. temporal LASCA: 0.31+/-0.03 vs. 1.36+/-0.09) and finally leads to higher resolution spatiotemporal patterns of cerebral blood flow.
在本文中，我们发展了一种随机过程理论来解释激光散斑现象。推导了散斑积分强度随机过程Y(t)的概率分布与相对速度v(t)之间的关系。基于该随机过程理论，传统的空间或时间激光散斑对比度分析（即空间或时间LASCA）可分别作为空间或时间估计器推导得出。空间LASCA和时间LASCA都因空间或时间域中统计量不足和非平稳性而受到噪声影响。此外，两种LASCA都会导致空间或时间分辨率降低。提出了一种新的随机过程估计器，它能够克服这些缺点。在一项体外研究中，随机过程估计器的表现优于空间LASCA或时间LASCA，提供了更高的信噪比（随机过程估计器与空间LASCA与时间LASCA：33.64±6.87（均值±标准差）与9.08±2.85与3.83±1.05）。在一项体内结构成像研究中，随机过程估计器有效地抑制了对比度图像中的噪声，并因此提高了小血管的可分辨性。在一项关于大鼠后爪刺激诱发的体感皮层脑血流变化的功能成像研究中，随机过程估计器在单次试验数据中提供了低得多的数据估计误差（随机过程估计器与时间LASCA：0.31±0.03与1.36±0.09），最终得到更高分辨率的脑血流时空模式。
