论文标题
DIFFEORAPTOR：使用Raptor的差异模式间图像登记
DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR
论文作者
论文摘要
目的：在许多医学成像应用中，差异图像注册至关重要。已经提出了几种这种类型的注册算法，但主要用于对比内对齐。当前，在众多应用中至关重要的有效模式间/对比度差异注册仍然是一项艰巨的任务。方法：我们提出了一种新型的模式间/对比度登记算法，该算法利用稳健的贴片相关比（RAPTOR）指标允许允许模式间/对比图像对准和频带的地理射击，以傅立叶近似lie代数（flash）algorithm（flash）algorithm进行快速扩散量表。结果：所提出的算法（称为Diffeoraptor）通过三个公共数据库进行了验证，用于大脑和腹部图像注册任务，同时将结果与三种最先进的技术进行比较，包括Flash，NiftyReg和对称图像归一化（SYN）。结论：我们的结果表明，在注册准确性方面，Diffeoraptor提供了可比或更好的注册性能。此外，在模式间和对比度登记中，差异型会产生比SYN的更光滑的变形。 DiffeorAptor的代码可在https://github.com/nimamasoumi/diffeoraptor上公开获得。
Purpose: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. Methods: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio (RaPTOR) metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN). Conclusions: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor.
