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.
Despite exquisite soft tissue visualization and a significant impact on health care in the 40 years since it's conceptualization, MRI as it is used today still has significant limitations due to its inherent low sensitivity. Since sensitivity in MR increases with field strength, there has been considerable interest in moving to ever higher magnetic fields. However, scanning using ultra high field (UHF) MRI systems has proven difficult because of the increased inhomogeneities and higher SAR. The goal of this project is to extend the novel paradigm of imaging, MR fingerprinting (MRF), to UHF systems 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.
| Yang, Mingrui; Ma, Dan; Jiang, Yun et al. (2018) Low rank approximation methods for MR fingerprinting with large scale dictionaries. Magn Reson Med 79:2392-2400 |
| Obmann, Verena C; Pahwa, Shivani; Tabayayong, William et al. (2018) Diagnostic Accuracy of a Rapid Biparametric MRI Protocol for Detection of Histologically Proven Prostate Cancer. Urology 122:133-138 |
| Chen, Yong; Lo, Wei-Ching; Hamilton, Jesse I et al. (2018) Single breath-hold 3D cardiac T1 mapping using through-time spiral GRAPPA. NMR Biomed 31:e3923 |
| Ma, Dan; Jiang, Yun; Chen, Yong et al. (2018) Fast 3D magnetic resonance fingerprinting for a whole-brain coverage. Magn Reson Med 79:2190-2197 |
| McGivney, Debra; Deshmane, Anagha; Jiang, Yun et al. (2018) Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting. Magn Reson Med 80:159-170 |
| Zhao, Bo; Setsompop, Kawin; Adalsteinsson, Elfar et al. (2018) Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling. Magn Reson Med 79:933-942 |
| Panda, Ananya; Mehta, Bhairav B; Coppo, Simone et al. (2017) Magnetic Resonance Fingerprinting-An Overview. Curr Opin Biomed Eng 3:56-66 |
| Ma, Dan; Coppo, Simone; Chen, Yong et al. (2017) Slice profile and B1 corrections in 2D magnetic resonance fingerprinting. Magn Reson Med 78:1781-1789 |
| Badve, C; Yu, A; Dastmalchian, S et al. (2017) MR Fingerprinting of Adult Brain Tumors: Initial Experience. AJNR Am J Neuroradiol 38:492-499 |
| Jiang, Yun; Ma, Dan; Jerecic, Renate et al. (2017) MR fingerprinting using the quick echo splitting NMR imaging technique. Magn Reson Med 77:979-988 |
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