Le2i FRE2005, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, Dijon, France.
Anti-Cancer Center Georges-François Leclerc, Dijon, France.
Med Biol Eng Comput. 2018 Sep;56(9):1531-1539. doi: 10.1007/s11517-018-1797-0. Epub 2018 Feb 7.
PET images deliver functional data, whereas MRI images provide anatomical information. Merging the complementary information from these two modalities is helpful in oncology. Alignment of PET/MRI images requires the use of multi-modal registration methods. Most of existing PET/MRI registration methods have been developed for humans and few works have been performed for small animal images. We proposed an automatic tool allowing PET/MRI registration for pre-clinical study based on a two-level hierarchical approach. First, we applied a non-linear intensity transformation to the PET volume to enhance. The global deformation is modeled by an affine transformation initialized by a principal component analysis. A free-form deformation based on B-splines is then used to describe local deformations. Normalized mutual information is used as voxel-based similarity measure. To validate our method, CT images acquired simultaneously with the PET on tumor-bearing mice were used. Results showed that the proposed algorithm outperformed affine and deformable registration techniques without PET intensity transformation with an average error of 0.72 ± 0.44 mm. The optimization time was reduced by 23% due to the introduction of robust initialization. In this paper, an automatic deformable PET-MRI registration algorithm for small animals is detailed and validated. Graphical abstract ᅟ.
PET 图像提供功能数据，而 MRI 图像提供解剖信息。合并这两种模式的互补信息有助于肿瘤学研究。PET/MRI 图像的配准需要使用多模态配准方法。大多数现有的 PET/MRI 配准方法都是针对人体开发的，很少有针对小动物图像的工作。我们提出了一种基于两级分层方法的自动工具，用于进行临床前研究的 PET/MRI 配准。首先，我们对 PET 体数据应用非线性强度变换进行增强。全局变形通过主成分分析初始化的仿射变换进行建模。然后，使用基于 B 样条的自由变形来描述局部变形。归一化互信息用作基于体素的相似性度量。为了验证我们的方法，我们使用同时采集的肿瘤小鼠的 CT 图像。结果表明，与没有 PET 强度变换的仿射和变形配准技术相比，所提出的算法具有平均误差 0.72±0.44mm 的优势。由于引入了稳健初始化，优化时间减少了 23%。本文详细介绍并验证了一种用于小动物的自动变形 PET-MRI 配准算法。
