Li Dengwang, Li H, Yin Y
College of Physics and Electronics, Shandong Normal University, China.
Department of Radiation Oncology, Shandong Tumor Hospital and Institute, China.
Med Phys. 2012 Jun;39(6Part7):3675. doi: 10.1118/1.4734935.
The aiming is to improve the mutual information based registration method for PET-CT scanner, which faces the challenges of high computational complexity and high likelihood of being trapped into local optima due to an absence of spatial information.
In this work, new multi-scale registration framework called EPMR was proposed based upon an edge preserving total variation L1 norm (TV-L1) scale space representation. TV-L1 scale space is constructed by selecting edges and contours of images according to their size rather than the intensity values of the image features. This ensures more meaningful spatial information with EPMR framework for MI based registration. Furthermore, we design an optimal estimation of the TV-L1 parameter in EPMR framework by training and minimizing the transformation offset between the registered pairs for automated registration in medical system. We validated our EPMR method on multi-modal medical datasets from clinical studies with a combined PET/CT scanner. We compared our registration framework with other traditional registration approaches.
Our experimental results demonstrated that our method outperformed other methods in terms of the accuracy and robustness for medical images. EPMR can always achieve small offset value, which is more close to the ground truth, and the speed can be increased by 10%-14% comparing with other method for PET-CT registration under the same condition. Furthermore, clinical application by adaptive GTV (gross tumor volume) re-contouring for clinical PET/CT image guided radiation therapy throughout the course of radiotherapy is also studied, and the overlap between the automatically generated contours for CT image and the contours delineated by the oncologist using for theplanning system are on an average 90%.
In this work, we presented a novel scale space decomposition methodology to improve the traditional scale space decomposition and performance of Mutual information based image registration for PET-CT scanner.
旨在改进正电子发射断层显像-计算机断层扫描（PET-CT）扫描仪基于互信息的配准方法，该方法面临计算复杂度高以及因缺乏空间信息而极易陷入局部最优的挑战。
在这项工作中，基于保留边缘的全变分L1范数（TV-L1）尺度空间表示，提出了一种名为EPMR的新型多尺度配准框架。TV-L1尺度空间是通过根据图像的大小而非图像特征的强度值来选择图像的边缘和轮廓构建的。这为基于互信息的配准的EPMR框架确保了更有意义的空间信息。此外，我们通过训练并最小化医学系统中自动配准的配准对之间的变换偏移，在EPMR框架中设计了TV-L1参数的最优估计。我们在使用联合PET/CT扫描仪的临床研究的多模态医学数据集上验证了我们的EPMR方法。我们将我们的配准框架与其他传统配准方法进行了比较。
我们的实验结果表明，在医学图像的准确性和鲁棒性方面，我们的方法优于其他方法。EPMR总能实现较小的偏移值，更接近真实值，并且在相同条件下与其他PET-CT配准方法相比，速度可提高10%-14%。此外，还研究了在整个放射治疗过程中通过自适应大体肿瘤体积（GTV）重新勾画轮廓用于临床PET/CT图像引导放射治疗的临床应用，并且CT图像自动生成的轮廓与肿瘤学家用于治疗计划系统勾画的轮廓之间的平均重叠率为90%。
在这项工作中，我们提出了一种新颖的尺度空间分解方法，以改进传统的尺度空间分解以及PET-CT扫描仪基于互信息的图像配准的性能。
