Magnetic Resonance Fingerprinting (MRF) for Improved High Field MR

描述（由申请人提供）：磁共振被广泛使用，因为其能够生成对多种重要组织特性敏感的精致图像，但其固有的灵敏度较低。由于MR的灵敏度随磁场强度增加而增加，因此人们对向更高的磁场移动有相当大的兴趣。然而，使用超高场（UHF）MRI系统进行扫描已被证明是困难的，这是因为主静磁场和射频（RF）场两者中的不均匀性以及在更高场处增加的比吸收率（SAR）。近二十年来，各种研究人员试图在7 T下产生均匀的B 0和B1场。然而，众所周知，在这一点上，电磁物理学并没有为这个问题提供一个通用的解决方案，因此UHF MRI在很大程度上是“卡住”作为一种研究工具，与广泛的临床成像翻译的前景暗淡。而不是专注于新技术的产生均匀的领域，我们提出了一个根本的重新思考如何MR对比信息的采集，这将带来前所未有的灵敏度超高频MRI承担在临床上。我们最近引入了MR指纹（MRF）的概念;这是一种完全不同的MR数据采集和后处理方法。代替使用固定脉冲序列进行采集，MRF从压缩感测（CS）中借用概念并且使用伪随机采集，该伪随机采集使得来自不同材料或组织的信号具有独特的信号演变或“指纹”，该信号演变或“指纹"同时是许多期望的材料属性的函数。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字典相匹配。然后可以将这些转换为感兴趣的MR参数的定量图。由于这种视角的改变，磁流变液中不需要均匀场。事实上，这些不均匀性可以用于更快的成像和更独特的像素签名，从而改善模式识别。该项目的具体目标都集中在将这种有前途的技术转化和扩展到UHF MRI的实际应用中。如果成功，这些方法可以实现提高UHF灵敏度的最初承诺，并使UHF MRI广泛用于常规临床工作，同时也为神经成像提供了新的机会。

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.