MR image reconstruction for non-Cartesian data and quantitative susceptibility maps using generalized sampling

该项目旨在改进非笛卡尔磁共振数据的数据采集和重建，特别是在定量磁化率成像的背景下。越来越多的磁共振数据是以非笛卡尔方式获取的，这与经典的笛卡尔数据采集相比具有许多优点，而定量敏感性成像这一相当新的领域正越来越多地应用于神经放射学领域，如脑肿瘤、髓鞘和出血的成像。虽然已经报道了有希望的初步结果，但深入观察，仍存在许多困难。例如，笛卡尔序列容易受到运动伪影的影响，需要很长的采集时间，而重建算法容易受到伪影的影响。在该项目中，应开发健壮的数据采集和无伪影的重建。该项目包括数据采集和重建方面。对广义采样重构的最优采样方案进行仿真，并在捕获序列中实现。利用小波和剪切波，设计了基于广义采样的重构方案。这些方案还将用于重建定量敏感性图。此外，非傅立叶编码的数据应该用再现先前使用的子波和剪切波的RF脉冲来获取。该非傅立叶编码的数据将由先前开发的重建算法依次重建。

This projects aims to improve data acquisition and reconstruction for non-Cartesian MR-data, especially in the context of quantitative susceptibility imaging. An increasing amount of MR data is acquired non-Cartesianly which has various advantages compared to a classical Cartesian data acquisition, and the rather new field of quantitative susceptibility imaging is getting applied more and more in the area of neuroradiology, e.g. in imaging brain tumors, myelin and hemorrhage. Although promising first results have been reported, when viewed in depth, there are many difficulties. For example, the Cartesian sequences are susceptible to motion artifacts and require long acquisition times, and reconstruction algorithms are prone to artifacts. In this project, robust data acquisition and reconstructions which are free of artifacts shall be developed. This project includes aspects of data acquisition and reconstruction. Optimal sampling schemes for the generalized sampling reconstruction are to be simulated and implemented in acquisition sequences. Using wavelets and shearlets, reconstruction schemes based on generalized sampling will be designed. These schemes will also be used for the reconstruction of quantitative susceptibility maps. Furthermore, non-Fourier encoded data should be acquired with RF-pulses reproducing the previously used wavelets and shearlets. This non-Fourier encoded data shall, in turn, be reconstructed by the previously developed reconstruction algorithms.