Abstract
Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.
Highlights
Quantitative magnetic resonance (MR) relaxometry can quantify the relaxation time (e.g., T1, T2, T2* relaxation time) to clarify the physical and pathological properties of human tissues [1]
Clinical applications of quantitative MR relaxometry are limited by the length of the imaging procedure required to estimate the tissue relaxation time; motion artifacts can interfere with the results, and the procedure does not meet the needs for clinical scheduling efficiency
signal-to-noise ratio (SNR) the validation dataset was selected as the optimal model
Summary
Quantitative magnetic resonance (MR) relaxometry can quantify the relaxation time (e.g., T1, T2, T2* relaxation time) to clarify the physical and pathological properties of human tissues [1]. Quantitative MR relaxometry was reported to increase accuracy and precision compared with conventional weighted magnetic resonance imaging (MRI) in detecting lesions, and it can even synthesize traditional weighted images [2,3]. Clinical applications of quantitative MR relaxometry are limited by the length of the imaging procedure required to estimate the tissue relaxation time; motion artifacts can interfere with the results, and the procedure does not meet the needs for clinical scheduling efficiency. Magnetic resonance fingerprinting (MRF) is an approach for designing the rapid quantitative sequence [4]. MRF has the advantage of providing quantitative images of multiple types of relaxation times simultaneously in a relatively short imaging time (several minutes). Because MRF image reconstruction requires comparison with a vast computer simulation database (dictionary matching), the extended image reconstruction time has become a considerable challenge in the development of MRF [5]
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