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.
Healthy blood flow is necessary for the health of all organs. As such, it is not surprising that blood flow is a very powerful indicator of tissue health and its activity. Indeed, recent evidence suggests 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. Thus, there is a clear need for tools to image blood flow and other related parameters non-invasively. Such a method could be a powerful biomarker for early diagnosis and for assessing treatment efficacy in these and other diseases. It would also be a very powerful technique for researchers investigating brain function. In that regard, arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique to image perfusion without injection of contrast agents. Instead of contrast agents, ASL uses radiofrequency waves to create a tracer by magnetically labeling the blood itself. ASL is rapidly gaining prominence in the clinic because perfusion can yield crucial information about the health of brain tissue and its vasculature, and it is being adopted to image other organs as well with increasing frequency. ASL could also be an invaluable tool to study cognitive function, psychiatric disorders and their treatment. Unfortunately, while ASL's potential as a clinical and research tool is phenomenal, the technique is not easy to implement and is still severely challenged by poor image quality (i.e., low signal to noise ratio - SNR) and resolution, particularly in the brain's white matter. Given that reliable ASL imaging of the white matter is very desirable as a biomarker for white matter diseases, this proposal seeks to tackle these challenges by developing a new strategy for ASL imaging. Success in this proposal will translate into a new robust perfusion imaging technique that can be made readily available in most clinical and research MRI scanners. The new technique will permit physicians and scientists to obtain quantitative images of multiple vascular parameters, such as blood flow and blood volume. These quantitative images will provide fundamental data to diagnose and characterize the disease early in the process, as well as to determine treatment efficacy.
| Hernandez-Garcia, Luis; Lahiri, Anish; Schollenberger, Jonas (2018) Recent progress in ASL. Neuroimage : |
| Wright, Katherine L; Jiang, Yun; Ma, Dan et al. (2018) Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling. Magn Reson Imaging 50:68-77 |
| Panda, Ananya; Mehta, Bhairav B; Coppo, Simone et al. (2017) Magnetic Resonance Fingerprinting-An Overview. Curr Opin Biomed Eng 3:56-66 |
