Quantitative Blood Flow Imaging using Spin Labeled MR Fingerprinting

 描述（由申请人提供）：显然需要一种强大的方法来对脑血流动力学进行成像，以更好地了解疾病过程，以及开发早期疾病生物标志物。许多疾病如癌症和中风的特征在于灌注不足和其他血流相关参数的变化。还有越来越多的证据表明，脑血流调节不良与几种疾病的发生密切相关，这些疾病的患病率正在上升，如多发性硬化症和阿尔茨海默病。而常规的临床灌注成像是麻烦的并且需要注射造影剂。动脉自旋标记（ASL）是一种磁共振成像（MRI）技术，可在不注射造影剂的情况下对灌注进行成像。ASL在临床上迅速获得重视，因为灌注可以产生有关脑组织及其血管系统健康的关键信息，尽管它也越来越频繁地用于其他器官。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）的严重挑战。这些挑战在白色物质中加剧，因为其低灌注速率和从标记部位到体素的较长传输时间。在这项提案中，我们的目标是开发一个新的采集和定量成像灌注和其他生理相关参数的框架。该框架利用了新开发的MR指纹识别技术。我们的目标是优化这个框架，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，我们的目标是通过比较独立的测量相同的参数，使用替代，建立的方法来验证该技术。

 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.