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Magnetic Resonance Fingerprinting (MRF) for Improved High Field MR

用于改进高场 MR 的磁共振指纹识别 (MRF)

基本信息

批准号：
9107869
负责人：
Mark Griswold
金额：
$ 61.64万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-15 至 2018-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Magnetic Resonance is widely used because of its ability to generate exquisite images sensitive to multiple important tissue properties, but it is inherently low in sensitivity. Since sensitivity in MR increases with field strength, there has bee considerable interest in moving to ever higher magnetic fields. However, scanning using ultra high field (UHF) MRI systems has proven difficult because of inhomogeneities in both the main static magnetic field and the radiofrequency (RF) fields and the increased specific absorption rate (SAR) at higher fields. For nearly two decades, various investigators have tried to generate homogeneous B0 and B1 fields at 7T. However, it is well known at this point that electromagnetic physics does not provide a general solution for this problem, and thus UHF MRI is largely "stuck" as a research tool, with dim prospects for the translation to widespread clinica imaging. Instead of focusing on new technology for the generation of homogeneous fields, we propose a fundamental re-thinking of how MR contrast information is acquired which will bring the unprecedented sensitivity of UHF MRI to bear in the clinic. We recently introduced the concept of MR Fingerprinting (MRF); a completely different approach to MR data acquisition and post-processing. Instead of using a fixed pulse sequence for acquisition, MRF borrows concepts from compressed sensing (CS) and uses a pseudorandomized acquisition that causes the signals from different materials or tissues to have a unique signal evolution, or "fingerprint, that is simultaneously a function of many desired material properties. The processing after acquisition involves a pattern recognition algorithm to match the fingerprints to a predefined dictionary of predicted signal evolutions. These can then be translated into quantitative maps of the MR parameters of interest. Because of this change in perspective, there is no requirement of homogeneous fields in MRF. In fact, these inhomogeneities can be exploited for faster imaging and more unique pixel signatures and thus improved pattern recognition. The specific aims for the project are all focused on translating and extending this promising technology to practical use in UHF MRI. When successful, these methods could deliver on the original promise of increased sensitivity in UHF and enable the widespread adoption of UHF MRI for routine clinical work, while also offering new opportunities for neuroimaging.

描述(申请人提供)：磁共振被广泛使用，因为它能够产生对多种重要组织特性敏感的精致图像，但它固有的低灵敏度。由于磁共振的灵敏度随着磁场强度的增加而增加，因此人们对转移到更高的磁场中有相当大的兴趣。然而，由于主静磁场和射频(RF)场的不均匀以及高场比吸收率(SAR)的增加，使用超高场(UHF)MRI系统进行扫描已被证明是困难的。近二十年来，不同的研究人员一直试图在7T时产生均匀的B0和B1场。然而，众所周知，电磁物理学并没有为这个问题提供通用的解决方案，因此超高频磁共振成像作为一种研究工具在很大程度上被“卡住了”，转化为广泛的临床成像的前景黯淡。与其专注于产生均质场的新技术，我们建议从根本上重新思考如何获取磁共振对比信息，这将使超高频磁共振成像前所未有的敏感性应用于临床。我们最近引入了磁共振指纹(MRF)的概念，这是一种完全不同的磁共振数据采集和后处理方法。MRF不是使用固定的脉冲序列进行采集，而是借用压缩传感(CS)的概念，并使用伪随机化采集，使来自不同材料或组织的信号具有独特的信号演变，即同时是许多所需材料特性的函数的“指纹”。采集后的处理涉及模式识别算法，以将指纹与预测信号演变的预定义词典进行匹配。然后可以将这些转换成感兴趣的MR参数的定量地图。由于这种视角的改变，在MRF中不需要均匀的场。事实上，可以利用这些不均匀性来进行更快的成像和更独特的像素签名，从而改进模式识别。该项目的具体目标都集中在将这一有前途的技术转化为实际应用于超高频磁共振成像。如果成功，这些方法可以兑现最初承诺的提高超高频灵敏度的承诺，并使超高频磁共振在常规临床工作中得到广泛采用，同时也为神经成像提供了新的机会。