Optimization of Magnetic Resonance Fingerprintingusing Quantum Inspired Algorithms

摘要 磁共振指纹(MRF)是一种能够产生组织图谱的定量技术 物业价值在单一和快速的收购。MRF已被证明对两者的细微变化很敏感 大脑、前列腺、乳房和腹部的正常和病变组织，但需要提高敏感性 临床应用。需要对MRF收购进行全面优化，以实现更高的 敏感度和更快的收购。我们提出应用量子优化(QIO)技术来解决 磁流变液的优化问题。QIO方法在处理大型和非凸问题时是有效的，例如 我们建议应用这些算法来优化两个序列参数，如翻转角， 重复时间、回声时间以及采样轨迹。待优化的目标函数将 被设计为包括诸如信号幅度、模式匹配度量以及T1和T2误差等特征。 序列将在体模和体内进行测试，并与目前关于MRF优化的文献进行比较。是这样的 还没有对磁流变液进行全面的优化，通过应用这些新的计算方法 方法，我们将获得一个更快、对组织特性变化更敏感的MRF序列 用于疾病检测、表征和监测。

Abstract Magnetic resonance fingerprinting (MRF) is a quantitative technique that is able to produce maps of tissue property values in a single and rapid acquisition. MRF has been shown to be sensitive to subtle changes in both normal and diseased tissues in the brain, prostate, breast, and abdomen, yet increased sensitivity is desired for clinical applications. A comprehensive optimization of the MRF acquisition is required to achieve higher sensitivity and faster acquisitions. We propose to apply quantum inspired optimization (QIO) techniques to solve the problem of MRF optimization. QIO methods are effective in handling large and nonconvex problems such as this one, and we propose to apply these algorithms to optimize both sequence parameters such as flip angle, repetition time, and echo time, as well as the sampling trajectories. The objective function to be optimized will be designed to include characteristics such as signal magnitude, pattern matching metrics, and T1 and T2 errors. Sequences will be tested in phantom and in vivo, and compared to current literature on MRF optimization. Such a comprehensive optimization of MRF has not been performed, and by applying these novel computational methods, we will achieve a MRF sequence that is faster and is more sensitive to changes in tissue properties for the purposes of disease detection, characterization, and monitoring.