Farnia P, Makkiabadi B, Ahmadian A, Alirezaie J
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1167-1170. doi: 10.1109/EMBC.2016.7590912.
Intra-operative ultrasound as an imaging based method has been recognized as an effective solution to compensate non rigid brain shift problem in recent years. Measuring brain shift requires registration of the pre-operative MRI images with the intra-operative ultrasound images which is a challenging task. In this study a novel hybrid method based on the matching echogenic structures such as sulci and tumor boundary in MRI with ultrasound images is proposed. The matching echogenic structures are achieved by optimizing the Residual Complexity (RC) in the curvelet domain. At the first step, the probabilistic map of the MR image is achieved and the residual image as the difference between this probabilistic map and intra-operative ultrasound is obtained. Then curvelet transform as a sparse function is used to minimize the complexity of residual image. The proposed method is a compromise between feature-based and intensity-based approaches. Evaluation was performed using 14 patients data set and the mean of registration error reached to 1.87 mm. This hybrid method based on RC improves accuracy of nonrigid multimodal image registration by 12.5% in a computational time compatible with clinical use.
近年来，术中超声作为一种基于成像的方法，已被公认为是补偿非刚性脑移位问题的有效解决方案。测量脑移位需要将术前MRI图像与术中超声图像进行配准，这是一项具有挑战性的任务。在本研究中，提出了一种基于匹配MRI中脑沟和肿瘤边界等回声结构与超声图像的新型混合方法。通过在曲波域中优化残余复杂度（RC）来实现回声结构的匹配。第一步，获取MR图像的概率图，并得到作为该概率图与术中超声之间差值的残余图像。然后，使用作为稀疏函数的曲波变换来最小化残余图像的复杂度。所提出的方法是基于特征和基于强度的方法之间的一种折衷。使用14例患者的数据集进行了评估，配准误差的平均值达到1.87毫米。这种基于RC的混合方法在与临床使用兼容的计算时间内，将非刚性多模态图像配准的准确性提高了12.5%。
