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Deep learning based MR fingerprinting ASL ReconStruction (DeepMARS)1Tsinghua University, Beijing, China, 2Johns Hopkins University, Baltimore, MD, United States, 3New York University, New York, NY, United States, 4School of Biomedical Engineering and Imaging Sciences, King's College London, London, London, United Kingdom
Synopsis
The purpose of this study is to develop a MRF-ASL reconstruction algorithm using deep learning (DeepMARS). Compared with the traditional dictionary matching, our DeepMARS achieved higher intra-class correlation (ICC) in B1 (0.971 vs 0.921) and BAT (0.926 vs 0.761), similar ICC in T1 (0.957 vs 0.964) and CBF (0.936 vs 0.948) in the reproducibility test with much shorter calculation time per voxel (0.368 ms vs 2.899 s), suggesting that our DeepMARS may be a better alternative than the conventional MR dictionary matching approach.
Introduction:
Recently, an MR fingerprinting (MRF) based ASL technique (MRF-ASL) [1] was developed to take advantage of, rather than being limited by, the multiparametric nature of ASL. However, the reconstruction of MRF-ASL is time consuming, and the reproducibility needs to be improved. In this work, we demonstrate that deep learning based reconstruction method can achieve high reproducibility with short reconstruction time in MRF-ASL.Experiments:
Eight healthy subjects (age 26 3.7 years, 4 males and 4 females) were imaged using MRF-ASL sequence [1], and each subject was scanned three times to test the reproducibility, and three patients with Moyamoya disease (age 39 2.1years, 1 male) were included. The sequence parameters were: multi-slice EPI, matrix size = 64×64, voxel size = 2.8mm×2.8mmx10mm, 500 dynamics, scan duration=3.7min.Data analysis:
The MRF-ASL signal was considered under a single-compartment model [1] and the goal is to estimate four quantitative parameters simultaneously: B1, T1, cerebral blood flow (CBF), and bolus arrive time (BAT). The primarily focus of this study is to compare a novel deep learning (DL) based parametric estimation method to a traditional dictionary matching (DM) [2]:
DM method: First, a MRF dictionary was simulated with 6149000 combinations of B1 values in the range of 0.6~1.1 (in an increment of 0.02), T1 values in the range of 500~5000 ms (in an increment of 20 ms within 2000 ms, and 300 ms beyond 2000 ms), CBF values in the range of 3~150 ml/100g/min (in an increment of 3 ml/100g/min), and BAT values in the range of 300~3000 ms (in an increment of 50 ms). Then, for a given MRF signal time course, its cross-correlations (cc) with all entries in the dictionary were calculated and the highest cc was identified.
DL method: The architecture of our DeepMARS was illustrated in Fig. 1, and five models were trained: Model 1 used dictionary without noise and mean square error (MSE) as the loss function, Model 2 used the dictionary contaminated with Gaussian noise and MSE as loss function, Model 3 used the dictionary with Gaussian noise and mean absolute percentage error (MAPE) as loss function, Model 4 used Model 1 as the pre-trained model and was further trained by the dictionary with Gaussian noise and used MSE as loss function. Model 5 used Model 1 as the pre-trained model and was further trained by the dictionary with Gaussian noise and used MAPE as loss function. The signal to noise ratio of the Gaussian noise was randomly selected from 20 dB to 60 dB. The MSE was defined as: $$$({\parallel True_{Para}-Pred_{Para}\parallel_{2}})^2$$$, and the MAPE was defined as:$$$\sum_{p\in Para}{\left| True_{p}-Pred_{p}\right|}/True_{p}$$$, where $$$Para$$$ was $$$\left[B1, T2, CBF, BAT \right]$$$. The voxel-wise intra-class correlation (ICC) [3] was calculated for each slice to test the reproducibility. The calculation time per voxel of DM and DeepMARS was compared on a 3.2 GHz Inter Core i5 with 26 GB RAM.
Results and Discussion:
The ICCs ($$$mean\pm standard$$$) of four quantitative parameters for all methods were shown in Table 1. Compared with the DM method, the Model 1 showed lower ICCs in all four parameters, while Model 2/3/4/5 all showed higher ICCs in B1 and BAT, similar ICC in T1 and CBF, suggesting that the proposed DeepMARS outperformed the traditional DM method, and adding noise in the training promoted robustness of learning. It can also be observed that using pre-trained model improved the reproducibility of B1 and BAT (ICC: Model 4 > Model 2, Model 5 > Model 3), and the MAPE showed similar ICC of B1/T1/CBF and lower ICC of BAT compared with MSE (Model 3 vs Model 2, Model 5 vs Model 4). Notably, the calculation time of DM was more than 7500 times than that of DeepMARS in the same computer. Fig. 2 illustrated the results for all methods from one patient, who had bilateral MCA and right PCA stenosis. Accordingly, the prolonged BAT and lower CBF can be observed at both DM and DeepMARS methods. As shown in CBF mapping (Fig. 2), the MAPE loss function (Model 3 & 5) made it less noise in the delineated lesion than the MSE loss function (Model 2 & 4). Therefore, the Model 5 might be more suitable than other models for MRF-ASL reconstruction. Fig. 3 and Fig. 4 showed the comparison of DM and DeepMARS (using model 5) in one healthy subject and two patients. It can be seen that DeepMARS derived parametric maps showed better quality compared to DM method.Conclusion:
We have successfully demonstrated that the DeepMARS can provide MRF-ASL higher reproducibility in B1 and BAT, and similar reproducibility in T1 and CBF with much shorter calculation time than traditional dictionary matching method.Acknowledgements
No acknowledgement found.References
1. Pan, S., et al., Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL. Magnetic Resonance in Medicine, 2017. 78(5): p. 1812-1823.
2. Ma, D., et al., Magnetic resonance fingerprinting. Nature, 2013. 495(7440): p. 187-192.
3. McGraw, K. and S.P. Wong, Forming Inferences About Some Intraclass Correlation Coefficients. Vol. 1. 1996. 30-46.
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