双眼视差在立体视觉中发挥着重要的作用,然而到目前为止双眼视差引起的立体视觉如何激活大脑皮层仍没有得出一致结论,需进一步研究.因此设计了一组功能核磁共振(functional Magnetic Resonence Imaging,fMRI)实验来定位与立体视觉有关的大脑皮层,实验中使用改进的随机点立体图(Random D Stereogram,RDS)作为视觉刺激,采用块状实验设计并使用SPM8分析数据.结果如下:1)hV3A(V3辅助区上侧),LG(舌回)hMT/V5(第5视区上侧)LOS(枕外侧沟)以及VIPS(腹侧顶内沟)是主要激活区.2)背侧视觉通路激活强烈而腹侧视觉通路激活较少.由此推论:1)没有一个单独的区域或皮层可以完全解释立体视觉感知,立体视觉的形成是不同区域通力合作的结果.2)背侧视觉通路在处理立体视觉中发挥着重要的作用.实验中得到的激活区域与其他实验结果有些差异这里主要讨论大脑激活区域以及产生差异的原因,通过分析比较这些结果,立体视觉的机制可以得到进一步的理解.如果可以明确立体视觉正常者的激活区域,那么利用fMRI扫描来筛查弱视会更简单有效.
Many studies have shown that the magnetic resonance signal decay with an extend range of diffusion weighting(b-factor)is a bi-exponential attenuation in the diffusion-weighted magnetic resonance imaging experiments in vivo.Based on this feature and the two-compartmental model,we propose a twice-linear-fitting(TLF)algorithm to estimate the apparent diffusion coefficient(ADC)of the water molecules instead of the commonly used iterative Levenberg–Marquardt(LM)method.The TLF algorithm consists of two liner fitting steps to estimate the fast and the slow apparent diffusion coefficients and their sizes,respectively.It is unnecessary to guess the initial values in the whole fitting process.The time consumption of the TLF algorithm is much less than that of the iterative LM method.Moreover,the TLF algorithm may avoid the extraneous solutions,which often deteriorates the results of the LM method.Compared with the iterative fitting method,the TLF algorithm is a reliable and timeefficient approach to estimate the ADC of water molecules in vivo in magnetic resonance diffusion-weighted imaging experiments.
Song GaoFei WangDongqi HeShouyu ZhangShanglian Bao
Magnetic resonance imaging (MRI) has emerged as an invasive radiologic technique to assess and characterize cartilage lesions in the setting of injury and degenerative joint disease. However, most of the currently available clinical and research MRI techniques, including proton-density weighted fast spin echo (FSE) 1, T2-weighted FSE 2, T2 mapping 3, and steady state free precession imaging 4 have focused on the superficial layers of cartilage. There
