Evan Scope Crafts1, Hengfa Lu2, Huihui Ye3,4, Lawrence L Wald5,6,7, Bo Zhao1,2. 1. Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA. 2. Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA. 3. State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China. 4. Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China. 5. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA. 6. Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA. 7. Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Abstract
PURPOSE: To introduce a computationally efficient approach to optimizing the data acquisition parameters of MR Fingerprinting experiments with the Cramér-Rao bound. METHODS: This paper presents a new approach to the optimal experimental design (OED) problem for MR Fingerprinting, which leverages an early observation that the optimized data acquisition parameters of MR Fingerprinting experiments are highly structured. Specifically, the proposed approach captures the desired structure by representing the sequences of data acquisition parameters with a special class of piecewise polynomials known as B-splines. This incorporates low-dimensional spline subspace constraints into the OED problem, which significantly reduces the search space of the problem, thereby improving the computational efficiency. With the rich B-spline representations, the proposed approach also allows for incorporating prior knowledge on the structure of different acquisition parameters, which facilitates the experimental design. RESULTS: The effectiveness of the proposed approach was evaluated using numerical simulations, phantom experiments, and in vivo experiments. The proposed approach achieves a two-order-of-magnitude improvement of the computational efficiency over the state-of-the-art approaches, while providing a comparable signal-to-noise ratio efficiency benefit. It enables an optimal experimental design problem for MR Fingerprinting with a typical acquisition length to be solved in approximately 1 min. CONCLUSIONS: The proposed approach significantly improves the computational efficiency of the optimal experimental design for MR Fingerprinting, which enhances its practical utility for a variety of quantitative MRI applications.
PURPOSE: To introduce a computationally efficient approach to optimizing the data acquisition parameters of MR Fingerprinting experiments with the Cramér-Rao bound. METHODS: This paper presents a new approach to the optimal experimental design (OED) problem for MR Fingerprinting, which leverages an early observation that the optimized data acquisition parameters of MR Fingerprinting experiments are highly structured. Specifically, the proposed approach captures the desired structure by representing the sequences of data acquisition parameters with a special class of piecewise polynomials known as B-splines. This incorporates low-dimensional spline subspace constraints into the OED problem, which significantly reduces the search space of the problem, thereby improving the computational efficiency. With the rich B-spline representations, the proposed approach also allows for incorporating prior knowledge on the structure of different acquisition parameters, which facilitates the experimental design. RESULTS: The effectiveness of the proposed approach was evaluated using numerical simulations, phantom experiments, and in vivo experiments. The proposed approach achieves a two-order-of-magnitude improvement of the computational efficiency over the state-of-the-art approaches, while providing a comparable signal-to-noise ratio efficiency benefit. It enables an optimal experimental design problem for MR Fingerprinting with a typical acquisition length to be solved in approximately 1 min. CONCLUSIONS: The proposed approach significantly improves the computational efficiency of the optimal experimental design for MR Fingerprinting, which enhances its practical utility for a variety of quantitative MRI applications.
Authors: Nikola Stikov; Mathieu Boudreau; Ives R Levesque; Christine L Tardif; Joëlle K Barral; G Bruce Pike Journal: Magn Reson Med Date: 2014-02-27 Impact factor: 4.668
Authors: Yong Chen; Yun Jiang; Shivani Pahwa; Dan Ma; Lan Lu; Michael D Twieg; Katherine L Wright; Nicole Seiberlich; Mark A Griswold; Vikas Gulani Journal: Radiology Date: 2016-01-21 Impact factor: 11.105