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

使用广义采样对非笛卡尔数据和定量磁化率图进行 MR 图像重建

• 批准号: 336563999
• 负责人: Dr. Sina Straub
• 金额: --
• 依托单位: Department of Radiology
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/336563999?language=en
• 关键词: MR; image; reconstruction; non; Cartesian

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.

该项目旨在改善非笛卡尔MR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MR数据是以非笛卡尔方式采集的，与经典的笛卡尔数据采集相比，非笛卡尔方式具有各种优势，并且定量磁敏感成像的相当新的领域越来越多地应用于神经放射学领域，例如，对脑肿瘤、髓鞘和出血进行成像。虽然已经报告了有希望的初步结果，但如果深入观察，则存在许多困难。例如，笛卡尔序列易受运动伪影的影响并且需要长的采集时间，并且重建算法易于产生伪影。在本项目中，应开发无伪影的稳健数据采集和重建。该项目包括数据采集和重建方面。广义采样重建的最佳采样方案将在采集序列中进行模拟和实施。使用小波和剪切波，重建方案的基础上广义采样设计。这些方案也将用于重建定量磁化率图。此外，非傅立叶编码的数据应该用再现先前使用的小波和剪切波的RF脉冲来采集。该非傅立叶编码数据将依次通过先前开发的重建算法来重建。