Real-time adaptive sequential design for optimal acquisition of arterial spin labeling MRI data.

Journal article

An optimal sampling schedule strategy based on the Fisher information matrix and the D-optimality criterion has previously been proposed as a formal framework for optimizing inversion time scheduling for multi-inversion-time arterial spin labeling experiments. Optimal sampling schedule possesses the primary advantage of improving parameter estimation precision but requires a priori estimation of plausible parameter distributions that may not be available in all situations. An adaptive sequential design approach addresses this issue by incorporating the optimal sampling schedule strategy into an adaptive process that iteratively updates the parameter estimates and adjusts the optimal sampling schedule accordingly as data are acquired. In this study, the adaptive sequential design method was experimentally implemented with a real-time feedback scheme on a clinical MRI scanner and was tested in six normal volunteers. Adapted schedules were found to accommodate the intrinsically prolonged arterial transit times in the occipital lobe of the brain. Simulation of applying the adaptive sequential design approach on subjects with pathologically reduced perfusion was also implemented. Simulation results show that the adaptive sequential design approach is capable of incorporating pathologic parameter information into an optimal arterial spin labeling scheduling design within a clinically useful experimental time.

Authors: Xie, J; Clare, S; Gallichan, D; Gunn, R; Jezzard, P

• Publication status: Published
• Journal: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine
• Volume: 64
• Issue: 1
• Pages: 203-210
• Publication date: 2010-07-01
• DOI: 10.1002/mrm.22398
• EISSN: 1522-2594
• ISSN: 0740-3194
• Language: English
• Keywords: Male; Research Design; Female; Spin Labels; Models, Biological; Computer Simulation; Magnetic Resonance Imaging; Humans; Adult; Algorithms; Brain