Various multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Cone beam computed tomography (CBCT), computed tomography (CT) or Magnetic Resonance (MR) images thereby provides complementary functional and/or structural information of the targeted region and organs at risk. Merging this information relies on a correct spatial alignment of the observed anatomy between the acquired images. This can be achieved by the means of multi-modal deformable image registration (DIR), demonstrated to be capable of estimating dense and elastic deformations between images acquired by multiple imaging devices. However, due to the typically different field-of-view (FOV) sampled across the various imaging modalities, such algorithms may severely fail in finding a satisfactory solution. In the current study we propose a new fast method to align the FOV in multi-modal 3D medical images. To this end, a patch-based approach is introduced and combined with a state-of-the-art multi-modal image similarity metric in order to cope with multi-modal medical images. The occurrence of estimated patch shifts is computed for each spatial direction and the shift value with maximum occurrence is selected and used to adjust the image field-of-view. We show that a regional registration approach using voxel patches provides a good structural compromise between the voxel-wise and "global shifts" approaches. The method was thereby beneficial for CT to CBCT and MRI to CBCT registration tasks, especially when highly different image FOVs are involved. Besides, the benefit of the method for CT to CBCT and MRI to CBCT image registration is analyzed, including the impact of artifacts generated by percutaneous needle insertions. Additionally, the computational needs are demonstrated to be compatible with clinical constraints in the practical case of on-line procedures.
翻译:目前,在干预治疗工作流的不同步骤中涉及多种多式成像传感器。Cone波束计算断层摄影(CBCT)、计算断层成像(CT)或磁共振(MR)图像,从而为目标区域和风险器官提供了互补功能和/或结构信息。合并这一信息依赖于对所获取图像的观测解剖进行正确的空间调整。为此,采用多式变形图像登记(DIR)的方法可以实现这一点,证明它能够估算多式成像装置获得的图像之间的密度和弹性变异。然而,由于对不同成像模式进行抽样抽样取样的视野(FOV)图像,这种算法可能严重无法找到令人满意的解决办法。在目前的研究中,我们提出了一种新的快速方法,将FOVV在多式3D CB上观测到的解剖图象。为此,引入了一种基于补差的多式的多式变异式MCT图像,以适应多式立式的立体图像。由于通常的视野(FOV)外变异式图像(MCT)的变换方式,我们使用了一种最接近式的方法,从而显示了对正式的图像的变换的系统。