Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran.
Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran.
Sensors (Basel). 2022 Mar 21;22(6):2399. doi: 10.3390/s22062399.
Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.
脑移位是神经外科手术中图像引导应用的一个重要障碍。术中成像以更新图像引导手术系统的方法引起了越来越多的关注。然而，由于当前成像方式的固有局限性，准确的脑移位补偿仍然是一项具有挑战性的任务。在这项研究中，提出了术中光声成像的应用以及术中光声与术前磁共振图像的配准，以补偿脑变形。由于脑变形的不可预测性，找到令人满意的配准方法具有挑战性。在这项研究中，提出了用于光声-MR 图像配准的共稀疏分析模型，该模型可以捕捉两种模态之间的相互依赖性。所提出的算法基于通过交替方向乘子法学习的一对分析算子对映射变换的最小化来工作。该方法使用实验性体模和从小鼠脑中获得的离体数据进行了评估。体模数据的结果表明，与常用的归一化互信息方法相比，目标配准误差提高了约 63%。结果证明，当脑移位使术前 MRI 无效时，术中光声图像可能成为一种有前途的工具。
