Farnia P, Ahmadian A, Shabanian T, Serej N D, Alirezaie J
Image-Guided Intervention Group, Research Centre of Biomedical Technology and Robotics, RCBTR, Tehran University of Medical Sciences, Tehran, Iran.
Int J Comput Assist Radiol Surg. 2015 May;10(5):555-62. doi: 10.1007/s11548-014-1098-5. Epub 2014 Jul 4.
Compensation for brain shift is often necessary for image-guided neurosurgery, requiring registration of intra-operative ultrasound (US) images with preoperative magnetic resonance images (MRI). A new image similarity measure based on residual complexity (RC) to overcome challenges of registration of intra-operative US and preoperative MR images was developed and tested.
A new two-stage method based on the matching echogenic structures such as sulci is achieved by optimizing the residual complexity value in the wavelet domain between the ultrasound image and the probabilistic map of the MR image. The proposed method is a compromise between feature-based and intensity-based approaches. Evaluation was performed using a specially designed brain phantom and an in vivo patient data set.
The results of the phantom data set registration confirmed that the proposed objective function outperforms the accuracy of adapted RC for multi-modal cases by 48 %. The mean fiducial registration error reached 1.17 and 2.14 mm when the method was applied on phantom and clinical data sets, respectively.
This improved objective function based on RC in the wavelet domain enables accurate non-rigid multi-modal (US and MRI) image registration which is robust to noise. This technology is promising for compensation of intra-operative brain shift in neurosurgery.
图像引导神经外科手术通常需要对脑移位进行补偿，这就要求将术中超声（US）图像与术前磁共振图像（MRI）进行配准。开发并测试了一种基于残余复杂度（RC）的新图像相似性度量，以克服术中超声图像与术前磁共振图像配准的挑战。
通过优化超声图像与磁共振图像概率图在小波域中的残余复杂度值，实现了一种基于匹配脑沟等回声结构的新两阶段方法。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。使用专门设计的脑部模型和体内患者数据集进行评估。
模型数据集配准结果证实，所提出的目标函数在多模态情况下的准确性比适配的RC高出48%。当该方法应用于模型和临床数据集时，平均基准配准误差分别达到1.17和2.14毫米。
这种在小波域中基于RC的改进目标函数能够实现准确的非刚性多模态（超声和磁共振成像）图像配准，且对噪声具有鲁棒性。该技术有望用于神经外科手术中术中脑移位的补偿。
