4.5 Article

Mitigating undersampling errors in MR fingerprinting by sequence optimization

期刊

MAGNETIC RESONANCE IN MEDICINE
卷 89, 期 5, 页码 2076-2087

出版社

WILEY
DOI: 10.1002/mrm.29554

关键词

MR fingerprinting; optimal experiment design; quantitative MRI; undersampling

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This study proposes a method for optimizing the MR fingerprinting (MRF) sequence by taking into account both the applied undersampling pattern and a realistic reference map. By optimizing the flip angle sequence, the undersampling artifacts can be significantly reduced. Numerical simulations and in vivo measurements show that the optimized sequence exhibits better robustness against undersampling artifacts.
PurposeTo develop a method for MR Fingerprinting (MRF) sequence optimization that takes both the applied undersampling pattern and a realistic reference map into account. MethodsA predictive model for the undersampling error leveraging on perturbation theory was exploited to optimize the MRF flip angle sequence for improved robustness against undersampling artifacts. In this framework parameter maps from a previously acquired MRF scan were used as reference. Sequences were optimized for different sequence lengths, smoothness constraints and undersampling factors. Numerical simulations and in vivo measurements in eight healthy subjects were performed to assess the effect of the performed optimization. The optimized MRF sequences were compared to a conventionally shaped flip angle pattern and an optimized pattern based on the Cramer-Rao lower bound (CRB). ResultsNumerical simulations and in vivo results demonstrate that the undersampling errors can be suppressed by flip angle optimization. Analysis of the in vivo results show that a sequence optimized for improved robustness against undersampling with a flip angle train of length 400 yielded significantly lower median absolute errors in T1: 5.6%+/- 2.9% and T2: 7.9%+/- 2.3% compared to the conventional (T1: 8.0%+/- 1.9%, T2: 14.5%+/- 2.6%) and CRB-based (T1: 21.6%+/- 4.1%, T2: 31.4%+/- 4.4%) sequences. ConclusionThe proposed method is able to optimize the MRF flip angle pattern such that significant mitigation of the artifacts from strong k-space undersampling in MRF is achieved.

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