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摘要翻译：
正电子发射断层扫描(PET)的图像提供了灌注和代谢等功能数据。另一方面，来自磁共振成像(MRI)的图像提供了描述解剖结构的信息。融合这两种方法的互补信息在肿瘤学中是有帮助的。在这个项目中，我们实现了一个完整的工具，允许在临床前研究中对小动物成像进行半自动的MRI-PET配准。提出了一种两阶段分层配准方法。首先，应用全局仿射配准。为了保证配准的鲁棒性和快速性，利用主成分分析(PCA)计算全局仿射配准的初始参数。由于在MRI扫描中只有PET体积中的低强度显示了解剖信息，我们提出了对PET体积进行非均匀强度变换，以增强低强度的对比度。这有助于通过增加低强度的贡献来改进质心和主轴的计算。然后，全局配准图像作为第二阶段的输入，即局部变形配准（B样条配准）。利用互信息作为度量函数进行优化。在这两个阶段都使用了多分辨率方法。注册算法由图形用户界面(GUI)和可视化方法支持，使得用户可以方便地与注册过程进行交互。两位医学专家在七个不同的腹部和大脑区域的数据集上验证了该配准算法的性能，包括噪声和困难的图像体积。
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英文标题：
《Sigmoid function based intensity transformation for parameter
initialization in MRI-PET Registration Tool for Preclinical Studies》
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作者：
Hiliwi Leake Kidane, Stephanie Bricq, Alain Lalande
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Images from Positron Emission Tomography (PET) deliver functional data such as perfusion and metabolism. On the other hand, images from Magnetic Resonance Imaging (MRI) provide information describing anatomical structures. Fusing the complementary information from the two modalities is helpful in oncology. In this project, we implemented a complete tool allowing semi-automatic MRI-PET registration for small animal imaging in the preclinical studies. A two-stage hierarchical registration approach is proposed. First, a global affine registration is applied. For robust and fast registration, principal component analysis (PCA) is used to compute the initial parameters for the global affine registration. Since, only the low intensities in the PET volume reveal the anatomic information on the MRI scan, we proposed a non-uniform intensity transformation to the PET volume to enhance the contrast of the low intensity. This helps to improve the computation of the centroid and principal axis by increasing the contribution of the low intensities. Then, the globally registered image is given as input to the second stage which is a local deformable registration (B-spline registration). Mutual information is used as metric function for the optimization. A multi-resolution approach is used in both stages. The registration algorithm is supported by graphical user interface (GUI) and visualization methods so that the user can interact easily with the process. The performance of the registration algorithm is validated by two medical experts on seven different datasets on abdominal and brain areas including noisy and difficult image volumes.
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PDF链接：
https://arxiv.org/pdf/1806.06469
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