Towards predicting the encoding capability of MR fingerprinting sequences |
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| Institution: | 1. The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, Suite W200, Atlanta, GA 30322, USA;2. Center for Biomedical Imaging Research, Tsinghua University, Beijing 100084, China;3. Department of Bioengineering, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA;1. Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy;2. GE Global Research, Munich, Germany;3. Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy;4. IRCCS Stella Maris Foundation, Pisa, Italy;5. IMAGO7 Foundation, Pisa, Italy;1. Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA;3. The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA;4. Siemens Medical Solutions USA Inc., 40 Liberty Boulevard, Malvern, PA 19355, USA;1. Siemens Healthcare, Erlangen, Germany;2. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;3. Departments of Biomedical Engineering and Radiology, University of Virginia, Charlottesville, VA, USA |
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| Abstract: | Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization. |
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