Salvado Olivier, Hillenbrand Claudia, Zhang Shaoxiang, Wilson David L
Department of Biomedical Engineering, Case western Reserve University, 10900 Euclid Ave., Cleveland, OH 44122, USA.
IEEE Trans Med Imaging. 2006 May;25(5):539-52. doi: 10.1109/TMI.2006.871418.
We are developing methods to characterize atherosclerotic disease in human carotid arteries using multiple MR images having different contrast mechanisms (T1W, T2W, PDW). To enable the use of voxel gray values for interpretation of disease, we created a new method, local entropy minimization with a bicubic spline model (LEMS), to correct the severe (approximately 80%) intensity inhomogeneity that arises from the surface coil array. This entropy-based method does not require classification and robustly addresses some problems that are more severe than those found in brain imaging, including noise, steep bias field, sensitivity of artery wall voxels to edge artifacts, and signal voids near the artery wall. Validation studies were performed on a synthetic digital phantom with realistic intensity inhomogeneity, a physical phantom roughly mimicking the neck, and patient carotid artery images. We compared LEMS to a modified fuzzy c-means segmentation based method (mAFCM), and a linear filtering method (LINF). Following LEMS correction, skeletal muscles in patient images were relatively isointense across the field of view. In the physical phantom, LEMS reduced the variation in the image to 1.9% and across the vessel wall region to 2.5%, a value which should be sufficient to distinguish plaque tissue types, based on literature measurements. In conclusion, we believe that the correction method shows promise for aiding human and computerized tissue classification from MR signal intensities.
我们正在开发利用具有不同对比机制（T1加权、T2加权、质子密度加权）的多幅磁共振图像来表征人类颈动脉粥样硬化疾病的方法。为了能够使用体素灰度值来解释疾病，我们创建了一种新方法，即基于双三次样条模型的局部熵最小化方法（LEMS），以校正由表面线圈阵列产生的严重（约80%）强度不均匀性。这种基于熵的方法不需要分类，并且能够有力地解决一些比脑成像中更严重的问题，包括噪声、陡峭的偏置场、动脉壁体素对边缘伪影的敏感性以及动脉壁附近的信号空洞。我们在具有逼真强度不均匀性的合成数字模型、大致模拟颈部的物理模型以及患者颈动脉图像上进行了验证研究。我们将LEMS与一种基于改进模糊c均值分割的方法（mAFCM）以及一种线性滤波方法（LINF）进行了比较。经过LEMS校正后，患者图像中的骨骼肌在整个视野范围内相对等信号。在物理模型中，LEMS将图像中的变化降低到1.9%，在血管壁区域降低到2.5%，根据文献测量，这个值应该足以区分斑块组织类型。总之，我们认为这种校正方法有望帮助从磁共振信号强度进行人体和计算机化的组织分类。
