Nonlinear mass-preserving registration for magnetic resonance imaging (MRI) and positron emission tomography (PET)

磁共振成像 (MRI) 和正电子发射断层扫描 (PET) 的非线性质量保存配准

• 批准号: 214620425
• 负责人: Professor Dr. Martin Burger
• 金额: --
• 依托单位: Projektträger DESY
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/214620425?language=en
• 关键词: Nonlinear; mass; preserving; registration; magnetic

Image registration is an important task in medical imaging. The problem is to automatically establish point-to-point correspondences between objects sensed at different times, with different devices, or from different perspectives. Image registration is essential for applications such as motion correction or data fusion. As image registration is an ill-posed inverse problem, proper modeling and additional constraints on the wanted transformation field become inevitable. Mass-preservation (MP) is one of those constraints that proved to be crucial for the correction of image distortions due to field inhomogeneities in Magnetic Resonance Imaging (MRI) and the respiratory and cardiac motion correction in thoracic Positron Emission Tomography (PET). A wide utilization of MP techniques in those medical applications requires, however, the solution of challenging mathematical questions to be addressed in this project. Existing MP techniques will hence be optimized with respect to numerical efficiency and implementation. Problem specific data fidelities will yield an improved robustness against noise and even simplifications of the algorithms. A key component yielding existence of solutions in MP setting is a regularization energy based on nonlinear elasticity. This outstanding regularizer and its numerical discretization will hence be further investigated in this project. A deep mathematical understanding of the developed models will be gained by using regularization theory for inverse problems.

图像配准是医学成像中的一项重要任务。问题是在不同时间、不同设备或不同视角感知的对象之间自动建立点对点的对应关系。图像配准对于运动校正或数据融合等应用至关重要。由于图像配准是一个不适定的反问题，适当的建模和额外的约束所需的转换领域成为不可避免的。质量保持（MP）是被证明对于校正由于磁共振成像（MRI）中的场不均匀性引起的图像失真以及胸部正电子发射断层扫描（PET）中的呼吸和心脏运动校正至关重要的那些约束之一。MP技术在这些医疗应用中的广泛利用需要，但是，在这个项目中要解决的具有挑战性的数学问题的解决方案。因此，现有的MP技术将优化数值效率和实施。特定问题的数据一致性将产生对噪声的改进的鲁棒性，甚至算法的简化。在MP设置的解的存在性产生的一个关键组成部分是一个正则化能量的基础上的非线性弹性。因此，这个杰出的正则化及其数值离散化将在本项目中进一步研究。一个深刻的数学理解的开发模型将获得通过使用正则化理论的反问题。

项目成果

Registration of Noisy Images via Maximum A-Posteriori Estimation

通过最大后验估计配准噪声图像

• DOI: 10.1007/978-3-319-08554-8_24
• 发表时间: 2014
• 作者: Suhr S;Tenbrinck D;Burger M;Modersitzki
• 通讯作者: Modersitzki

Color Bregman TV

• DOI: 10.1137/130943388
• 发表时间: 2013-10
• 期刊: SIAM J. Imaging Sci.
• 作者: Michael Möller;Eva-Maria Brinkmann;M. Burger;Tamara Seybold

Variational method for motion corrected reconstruction with MRI information in positron emission tomography

正电子发射断层扫描中利用MRI信息进行运动校正重建的变分法

• DOI: 10.1109/nssmic.2015.7582045
• 发表时间: 2016
• 期刊: 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
• 作者: Dirk Mannweiler;S. Suhr;J. Modersitzki;M. Burger
• 通讯作者: M. Burger