Magnetic Resonance Fingerprinting (MRF) for Improved High Field MR

描述（由申请人提供）：磁共振被广泛应用，因为它能够产生对多种重要组织特性敏感的精美图像，但它本身的灵敏度很低。由于磁流变学的灵敏度随磁场强度的增加而增加，人们对移动到更高的磁场产生了相当大的兴趣。然而，使用超高场（UHF） MRI系统进行扫描已经被证明是困难的，因为在主静态磁场和射频（RF）场中都存在不均匀性，并且在更高的场中增加了比吸收率（SAR）。近二十年来，各种研究人员试图在7T下产生均匀的B0和B1油田。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在很大程度上被“困”为一种研究工具，在转化为广泛的临床成像方面前景渺茫。我们不再关注产生均匀场的新技术，而是从根本上重新思考如何获取MR对比信息，这将使超高频MRI在临床中发挥前所未有的灵敏度。我们最近介绍了磁共振指纹（MRF）的概念；一种完全不同的MR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，而是借鉴了压缩传感（CS）的概念，并使用伪随机采集，使来自不同材料或组织的信号具有独特的信号演变，或“指纹”，这同时是许多所需材料特性的函数。采集后的处理涉及一种模式识别算法，将指纹与预测信号演化的预定义字典相匹配。然后，这些可以转化为感兴趣的MR参数的定量图。由于这种观点的改变，在MRF中不需要同质场。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，从而改进模式识别。该项目的具体目标都集中在将这项有前途的技术转化和扩展到UHF MRI的实际应用上。如果成功，这些方法可以实现最初的承诺，即提高超高频的灵敏度，并使超高频MRI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。

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.