Beomgu Kang1, Byungjai Kim1,2, Michael Schär2, HyunWook Park1, Hye-Young Heo2,3. 1. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. 2. Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA. 3. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.
Abstract
PURPOSE: To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging. METHODS: Pseudo-randomized RF saturation parameters and relaxation delay times were applied in an MR fingerprinting framework to generate transient-state signal evolutions for different MTC parameters. Prospectively compressed sensing-accelerated (four-fold) MR fingerprinting images were acquired from 6 healthy volunteers at 3 T. A convolutional neural network framework in an unsupervised fashion was designed to solve an inverse problem of a two-pool MTC Bloch equation, and was compared with a conventional Bloch equation-based fitting approach. The MTC images synthesized by the convolutional neural network architecture were used for amide proton transfer and nuclear Overhauser enhancement imaging as a reference baseline image. RESULTS: The fully unsupervised learning scheme incorporated with the two-pool exchange model learned a set of unique features that can describe the MTC-MR fingerprinting input, and allowed only small amounts of unlabeled data for training. The MTC parameter values estimated by the unsupervised learning method were in excellent agreement with values estimated by the conventional Bloch fitting approach, but dramatically reduced computation time by ~1000-fold. CONCLUSION: Given the considerable time efficiency compared to conventional Bloch fitting, unsupervised learning-based MTC-MR fingerprinting could be a powerful tool for quantitative MTC and CEST/nuclear Overhauser enhancement imaging.
PURPOSE: To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging. METHODS: Pseudo-randomized RF saturation parameters and relaxation delay times were applied in an MR fingerprinting framework to generate transient-state signal evolutions for different MTC parameters. Prospectively compressed sensing-accelerated (four-fold) MR fingerprinting images were acquired from 6 healthy volunteers at 3 T. A convolutional neural network framework in an unsupervised fashion was designed to solve an inverse problem of a two-pool MTC Bloch equation, and was compared with a conventional Bloch equation-based fitting approach. The MTC images synthesized by the convolutional neural network architecture were used for amide proton transfer and nuclear Overhauser enhancement imaging as a reference baseline image. RESULTS: The fully unsupervised learning scheme incorporated with the two-pool exchange model learned a set of unique features that can describe the MTC-MR fingerprinting input, and allowed only small amounts of unlabeled data for training. The MTC parameter values estimated by the unsupervised learning method were in excellent agreement with values estimated by the conventional Bloch fitting approach, but dramatically reduced computation time by ~1000-fold. CONCLUSION: Given the considerable time efficiency compared to conventional Bloch fitting, unsupervised learning-based MTC-MR fingerprinting could be a powerful tool for quantitative MTC and CEST/nuclear Overhauser enhancement imaging.
Authors: Stephen P Jordan; Siyuan Hu; Ignacio Rozada; Debra F McGivney; Rasim Boyacioğlu; Darryl C Jacob; Sherry Huang; Michael Beverland; Helmut G Katzgraber; Matthias Troyer; Mark A Griswold; Dan Ma Journal: Proc Natl Acad Sci U S A Date: 2021-09-30 Impact factor: 11.205
Authors: Peter C M van Zijl; Kevin Brindle; Hanzhang Lu; Peter B Barker; Richard Edden; Nirbhay Yadav; Linda Knutsson Journal: Curr Opin Chem Biol Date: 2021-07-20 Impact factor: 8.972