School of Information Sciences, Manipal University, Manipal, India.
Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, India.
Biomed J. 2017 Dec;40(6):329-338. doi: 10.1016/j.bj.2017.09.002. Epub 2017 Dec 27.
Imaging modalities in medicine gives complementary information. Inadequacy in clinical information made single imaging modality insufficient. There is a need for computer-based system that permits rapid acquisition of digital medical images and performs multi-modality registration, segmentation and three-dimensional planning of minimally invasive neurosurgical procedures. In this regard proposed article presents multimodal brain image registration and fusion for better neurosurgical planning.
In proposed work brain data is acquired from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) modalities. CT and MRI images are pre-processed and given for image registration. BSpline deformable registration and multiresolution image registration is performed on the CT and MRI sequence. CT is fixed image and MRI is moving image for registration. Later end result is fusion of CT and registered MRI sequences.
BSpline deformable registration is performed on the slices gave promising results but on the sequences noise have been introduced in the resultant image because of multimodal and multiresolution input images. Then multiresolution registration technique is performed on the CT and MRI sequence of the brain which gave promising results.
The end resultant fused images are validated by the radiologists and mutual information measure is used to validate registration results. It is found that CT and MRI sequence with more number of slices gave promising results. Few cases with deformation during misregistrations recorded with low mutual information of about 0.3 and which is not acceptable and few recorded with 0.6 and above mutual information during registration gives promising results.
医学中的成像模式提供了互补的信息。由于临床信息不足，单一的成像模式显得不够充分。因此，我们需要一个基于计算机的系统，该系统能够快速获取数字医学图像，并对微创手术进行多模式配准、分割和三维规划。在这方面，本文提出了多模态脑图像配准和融合，以更好地进行神经外科规划。
在本文的工作中，脑数据是从磁共振成像（MRI）和计算机断层扫描（CT）模式中获取的。对 CT 和 MRI 图像进行预处理，并进行图像配准。对 CT 和 MRI 序列进行 B 样条变形配准和多分辨率图像配准。CT 是固定图像，MRI 是配准的移动图像。最后，将 CT 和配准的 MRI 序列进行融合。
B 样条变形配准在切片上的表现令人满意，但在序列上，由于多模态和多分辨率输入图像的影响，在结果图像中引入了噪声。然后，对脑的 CT 和 MRI 序列进行多分辨率配准技术，结果令人满意。
通过放射科医生对最终融合图像进行验证，并使用互信息测量来验证配准结果。结果表明，具有更多切片的 CT 和 MRI 序列的结果更有前景。在一些配准错误的情况下，记录到的互信息约为 0.3，这是不可接受的，而在一些配准正确的情况下，记录到的互信息为 0.6 或更高，这是令人满意的。
