Salvado O, Hillenbrand Claudia, Wilson D
Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106.
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4302-5. doi: 10.1109/IEMBS.2005.1615416.
We are involved in a comprehensive program to characterize atherosclerotic disease using multiple MR images having different contrast mechanisms (T1W, T2W, PDW, magnetization transfer, etc.) of human carotid and animal model arteries. We use specially designed intravascular and surface array coils that give high signal-to-noise but suffer from sensitivity inhomogeneity and significant noise. We present here a new non-parametric method for correcting the images without assumption of the number of different tissues. Intensity inhomogeneity is modeled with cubic spline and is locally optimized using an entropy criterion. Validation has been performed on a specially design neck phantom as well as actual MR scans on patient neck. This same algorithm has been successfully applied to the correction of very high resolution, intravascular coil images. The steep bias is corrected sufficiently to aid human interpretation of gray scales. It should also make possible computerized tissue classification.
我们参与了一个综合项目，使用具有不同对比机制（T1加权、T2加权、质子密度加权、磁化传递等）的多幅磁共振图像来表征人类颈动脉和动物模型动脉的动脉粥样硬化疾病。我们使用专门设计的血管内和表面阵列线圈，这些线圈能提供高信噪比，但存在灵敏度不均匀性和大量噪声的问题。我们在此提出一种新的非参数方法，用于在不假设不同组织数量的情况下校正图像。强度不均匀性用三次样条进行建模，并使用熵准则进行局部优化。已在专门设计的颈部模型以及患者颈部的实际磁共振扫描上进行了验证。同样的算法已成功应用于校正超高分辨率的血管内线圈图像。陡峭的偏差得到了充分校正，有助于人类对灰度进行解读。这也应该使计算机化的组织分类成为可能。
