首页> 外文会议>Image Processing pt.2; Progress in Biomedical Optics and Imaging; vol.6 no.24 >Evaluation of sub-voxel registration accuracy between MRI and 3D MR spectroscopy of the brain
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Evaluation of sub-voxel registration accuracy between MRI and 3D MR spectroscopy of the brain

机译:大脑MRI和3D MR光谱之间亚体素配准准确性的评估

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摘要

The implementation of Magnetic Resonance Spectroscopic Imaging (MRSI) for diagnostic imaging benefits from close integration of the lower-spatial resolution MRSI information with information from high-resolution structural MRI. Since patients can commonly move between acquisitions, it is necessary to account for possible mis-registration between the datasets arising from differences in patient positioning. In this paper we evaluate the use of 4 common multi-modality registration criteria to recover alignment between high resolution structural MRI and 3D MRSI data of the brain with sub-voxel accuracy. We explore the use of alternative MRSI water reference images to provide different types of structural information for the alignment process. The alignment accuracy was evaluated using both synthetically created MRSI and MRI data and a set of carefully collected subject image data with known ground truth spatial transformation between image volumes. The final accuracy and precision of estimates were assessed using multiple random starts of the registration algorithm. Sub voxel accuracy was found by all four similarity criteria with normalized mutual information providing the lowest target registration error for the 7 subject images. This effort supports the ongoing development of a database of brain metabolite distributions in normal subjects, which will be used in the evaluation of metabolic changes in neurological diseases.
机译:用于诊断成像的磁共振波谱成像(MRSI)的实现得益于低空间分辨率MRSI信息与高分辨率结构MRI信息的紧密集成。由于患者通常可以在采集之间移动,因此有必要考虑由于患者位置不同而导致数据集之间可能存在的配准错误。在本文中,我们评估了使用4种常见的多模式配准标准来以亚体素精度恢复高分辨率结构MRI和3D MRSI大脑数据之间的对齐方式。我们探索使用替代MRSI水参考图像来为对齐过程提供不同类型的结构信息。使用合成创建的MRSI和MRI数据以及一组精心收集的对象图像数据(在图像体积之间具有已知的地面真实空间转换)来评估对准精度。使用注册算法的多个随机开始来评估估计的最终准确性和准确性。通过所有四个相似性标准发现亚体素准确性,并且归一化的互信息为7个对象图像提供了最低的目标配准误差。这项工作支持了正常受试者脑代谢物分布数据库的不断开发,该数据库将用于评估神经系统疾病的代谢变化。

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