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.

中文翻译：

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多模式图像中基于补丁的视野匹配，用于基于电穿孔的消融

当前，在介入治疗工作流程的不同步骤中涉及各种多模式成像传感器。因此，锥形束计算机断层扫描（CBCT），计算机断层扫描（CT）或磁共振（MR）图像可提供目标区域和处于危险中的器官的补充功能和/或结构信息。合并此信息取决于所获取图像之间观察到的解剖结构的正确空间对齐。这可以通过多模态可变形图像配准（DIR）来实现，该方法已被证明能够估计由多个成像设备采集的图像之间的密集和弹性变形。然而，由于跨各种成像模态采样的通常不同的视场（FOV），此类算法可能严重无法找到令人满意的解决方案。在当前的研究中，我们提出了一种新的快速方法来对齐多模式3D医学图像中的FOV。为此，引入了基于补丁的方法，并将其与最新的多模式图像相似性度量标准相结合，以应对多模式医学图像。为每个空间方向计算估计斑块偏移的发生，并选择出现次数最多的偏移值并将其用于调整图像视场。我们显示，使用体素补丁的区域配准方法在体素方式和“全局转移”方法之间提供了良好的结构折衷。因此，该方法对于CT到CBCT和MRI到CBCT配准任务特别有用，尤其是当涉及到高度不同的图像FOV时。除了，分析了CT到CBCT和MRI到CBCT图像配准的好处，包括经皮针头插入产生的伪影的影响。此外，在实际操作的在线过程中，计算需求被证明与临床约束兼容。