Quantitative Blood Flow Imaging using Spin Labeled MR Fingerprinting

 描述(由申请人提供)：显然需要一种强有力的方法来成像脑血流动力学，以更好地了解疾病过程，以及开发早期疾病生物标记物。许多疾病，如癌症和中风，其特点是血流灌注不足和其他血流相关参数的变化。越来越多的证据表明，脑血流调节不善与几种患病率呈上升趋势的疾病密切相关，比如多发性硬化症和阿尔茨海默病。而常规的临床灌注成像很麻烦，需要注射造影剂。动脉自旋标记(ASL)是一种磁共振成像(MRI)技术，无需注射造影剂即可成像。尽管ASL在其他器官中的使用频率也在增加，但ASL在临床上正迅速获得突出地位，因为灌注可以提供有关脑组织及其血管系统健康的关键信息。虽然ASL作为临床和研究工具的潜力是惊人的，但该技术仍然受到低信噪比(SNR)的严峻挑战。这些挑战在白质中加剧，因为它的低灌注率和从标记部位到体素的较长传输时间。在这项建议中，我们的目标是开发一种新的采集和量化框架，用于成像、灌注和其他生理相关参数。这一框架利用了最新开发的磁共振指纹技术。我们的目标是优化这一框架，以最大限度地提高灵敏度和准确性，同时减少扫描时间。最后，我们的目标是通过与使用替代的、已建立的方法对相同参数的独立测量进行比较来验证该技术。

 DESCRIPTION (provided by applicant): There is a clear need for a robust method to image cerebral hemodynamics to better understand disease processes, as well as to develop early stage disease biomarkers. Many diseases like cancer and stroke are characterized by deficits in perfusion and changes in other blood flow related parameters. There is also mounting evidence suggesting that poor regulation of cerebral blood flow is closely linked to the development of several diseases whose prevalence is on the rise, like multiple sclerosis and Alzheimer's disease. While routine clinical perfusion imaging is cumbersome and requires the injection of contrast agents. Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique to image perfusion without injection of contrast agents. ASL is rapidly gaining prominence in the clinic because perfusion can yield crucial information about the health of brain tissue and its vasculature, although it is being used with increasing frequency in other organs as well. While ASL's potential as a clinical and research tool is phenomenal, the technique is still severely challenged by low signal to noise ratio (SNR). These challenges are exacerbated in the white matter because of its low perfusion rate and longer transit time from the labeling site to the voxel. In this proposal we aim to develop a new acquisition and quantification framework for imaging perfusion and other physiologically relevant parameters. This framework takes advantage of newly developed MR fingerprinting techniques. We aim to optimize this framework in order to maximize the sensitivity and accuracy, while reducing the scan time. Finally, we aim to validate the technique by comparison to independent measurements of the same parameters using alternative, established methods.