Synopsis

We developed an artificial neural network (ANN) using magnitude 3-pool signal model based training sets. Simulations were performed for evaluation with various SNR and slice inhomogeneity (G_Z) levels. Two in-vivo data sets were tested. The results show decreased mean error and standard deviation when using the ANN model. The ANN model was more stable than the fitting method for different G_Z values. Moreover, the processing time of the ANN model took 140 times less than the fitting method.

Introduction

Method

1. Dataset

[Numerical Dataset]

Training dataset: 100,000 mGRE data were generated for the train-set using the parameters in $$ S_1(TE) = A_{my}*e^{-(1/T_{2,my}^{*})TE}+A_{ax}*e^{-(1/T_{2,ax}^{*})TE}+A_{ex}*e^{-(1/T_{2,ex}^{*})TE} ]$$ Specifically, the simulated signal was composed of three pool exponential signal with echo spacing of 1ms. A_my, A_ax, A_ex denote the amplitudes of myelin water, intracellular water, and extracellular water pools. T₂^*_,my, T₂^*,_ax, T₂^*_,ex are the T₂^* values of corresponding pools.

Testing dataset: For evaluation, a numerically simulated dataset was generated with the specification of Fig. 2. (d),(e).

[In-vivo data]

We acquired mGRE images from 2 subjects for testing. Specifically, Subject 1 was a healthy volunteer and Subject 2 was a patient with genetically confirmed X-linked adrenoleukodystrophy (X-ALD) disease. The imaging parameters for mGRE images were: TR=60ms, TE1=1.6ms, ΔTE=1ms, field of view =256×256×100mm³, spatial resolution = 1.6×1.6×2.0mm³. or Subject 2, an additional ihMT (magnetization transfer) imaging protocol was acquired for this subject and quantitative ihMT(qihMT) was generated from ihMT data (marked as MT in Figure 5.(d) )

2. Network Design

The neural network for the training was composed of 5 layers as shown in Fig. 1.

3. Evaluations

Simulations were performed to compare the performance of the ANN regarding the two aspects with conventional 3-pool model fitting^2,4. Also, visual comparison for in-vivo results was performed.

Robustness to noise: Performance was compared to various SNR levels (50, 150, 300) (Fig. 2. (d)).

Robustness to deviation from the model: Simulated data were compared assuming ΔB₀ slice inhomogeneity as⁵ $$ S_2(TE)= S_1(TE)*sinc(\gamma G_z \frac{\triangle z}{2}TE)$$ Here, γ is the gyromagnetic ratio, G_Z is the linear approximation of the field gradient in the slice-select direction, and ∆z is the slice thickness.

Ranges of the parameters for the simulation data are shown in Fig. 2. (e).

In-vivo application: The method was further applied voxel-wise for in-vivo images, which were not included in the training set. The processing time was measured. We used Intel Core I7-7500U CPU while the ANN model was processed by one GeForce GTX 1080 TI GPU with 11GB memory.

Results

Conclusion and Discussion

ANN based MWF mapping showed reduced mean error and standard deviation compared to the fitting method and were more robust to different G_Z, which may reflect the deviation from the model. In-vivo results of the ANN model also showed improved image quality, even though training was performed with simulation data only. Moreover, the ANN model is more time efficient than the fitting method, which may be beneficial for clinical application.

There are some limitations in our current study. The network seems to slightly overestimate MWF values within 10 to 20 and underestimate the values over 20. Similar behaviors were observed in ⁶ and might be due to the limitations of our model architecture. Moreover, in the current study, only simulation data was used for the training, which does not yet reflect situations in real in-vivo data such as susceptibility anisotropy, motion, and flow etc⁷. Thus adding in-vivo data for the training set may be beneficial.

Acknowledgements

References

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