Jieun Lee1, Joon Yul Choi2, Dongmyung Shin1, Se-Hong Oh3, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Cleveland Clinic, Epilepsy Center, Neurological Institute, Cleveland, OH, United States, 3Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea
Synopsis
In this study, the generalization capacity of the artificial neural
network for myelin water imaging (ANN-MWI) is explored by testing datasets with
different (1) scan protocols (resolution, RF shape, and TE), (2) noise levels, and (3)
types of disorders (NMO and edema). The ANN-MWI results show high
reliability in generating myelin water fraction maps from the datasets with
different resolution and noise levels. However, the
increased errors are reported for the datasets with the different RF shape,
TEs, and disorder type.
Purpose
Recently, Lee et al.1 proposed
an artificial neural network-based myelin water imaging (ANN-MWI)
reconstruction method and demonstrated real-time processing of MWI. ANN-MWI was
designed to generate a T2 distribution, from which myelin water
fraction (MWF) is calculated.2,3 For the training, multi-echo GRASE4
MWI data (FOV = 240×180×112 mm3; resolution = 1.5×1.5×4.0 mm3, sinc RF pulse, TE = 10
ms) of 12 subjects (6 healthy controls (HC) and 6 multiple sclerosis (MS)
patients) were utilized. In the work, the test dataset had the same
scan protocol, noise level, and types of diseases as in the training dataset.
Since the training and test datasets had the same settings, ANN-MWI generated high-quality
results for the test dataset. Recently, studies have suggested that the
performance of a neural network highly depends on the characteristics of the
training dataset.5,6 Therefore, further investigation is necessary to
test the generalization performance of ANN-MWI for datasets of different conditions.
In this study, we examine the reliability of ANN-MWI using the datasets of different
scan protocols (resolution, RF shape, and TE), noise levels, and types of
diseases (neuromyelitis optica (NMO) and edema) (Fig. 1).Methods
[Datasets]
The
following datasets were generated or acquired:
- Different scan protocols: The effects of different resolutions,
refocusing RF shape, and TEs were tested.
(1a) The original dataset (Ref 1) had a resolution
of 1.5×1.5×4.0
mm3. Two different resolutions, 2.0×2.0×4.0 mm3 and 2.5×2.5×4.0
mm3, were generated by truncating k-space in the eight test dataset (3 HC and 5 MS).
(1b) The original dataset
was acquired with a sinc-shaped refocusing RF (TBW = 4). To test the effects of
the RF shape, an SLR-designed refocusing pulse (TBW = 2) data were acquired in 14 HC.
(1c) The original dataset had a TE of 10 ms.
Two additional TE values (TE1 = 10.1 ms for 3 HC and 8 MS; TE1
= 10.2 ms for 5 HC and 6 MS) were tested.
- Different noise levels: Three different levels of
noise (one, two, and three times of noise standard deviation (STD) in each eight test data)7
were added to the 32-echo images of the dataset.
- Different types of disorders: The dataset of twenty-three
NMO patients (resolution = 1.5×1.5×4.0 mm3)8 and seven edema
patients (resolution = 1.7×1.7×5.0 mm3) were tested to check the
influence of disease types on the performance.
[Data
analysis]
To explore the reliability of ANN-MWI, all
datasets were processed using both conventional and ANN-MWI methods. The MWF
and normalized root-mean-square errors (NRMSE) of MWF were compared in a white
matter mask with the conventional MWI results as a reference.
Results
For the
eight test dataset of the
same parameters, the NRMSE of ANN-MWI was 2.26 ± 0.20%. For the different
scan protocols, the results are summarized in Figure 2. The mean NRMSEs of the
low-resolution datasets were 2.19
± 0.24% for 2.0×2.0×4.0 mm3 and 2.21 ± 0.25% for 2.5×2.5×4.0
mm3. Since
these errors are similar to that of the same resolution (2.26
± 0.20%), the
results suggest that the resolution does not affect the performance of ANN-MWI.
When the results of the different RF shape data were
examined, they reported significantly higher NRMSE of 4.81 ±
0.79%, indicating the influence of the RF shape. For the different TEs, NRMSEs were 4.02 ± 0.56%
for TE1 = 10.1 ms dataset and 7.90 ± 0.83% for TE1 = 10.2 ms dataset, showing significant
dependence of the errors on TE.
The MWF maps from the three different
noise-added data are compared in Figure 3a. The higher noise levels resulted in
noisier MWF maps. Despite the noisy maps, the results from the conventional
method and ANN-MWI show similar MWF maps (Fig. 3a), mean ± STD of MWF
values, and NRMSEs (Fig. 3b), suggesting consistent noise effects on both
methods.
In Figure 4, the MWF maps of the four
different subject types (HC, MS, NMO, and edema) are displayed with the error maps.
The average NRMSEs were 2.33 ±
0.06% in 3 HC, 2.21 ± 0.24% in 5 MS, 2.21 ±
0.32% in 23 NMO, and 3.84 ± 0.22% in the 7 edema
patients. Although both conventional and ANN-MWI methods generated similar MWF
maps, the difference map of the edema data reveals significant errors particularly
in the edema lesion. When the edema lesion was analyzed, the mean NRMSE was
9.59 ± 1.88%, demonstrating increased
NRMSE.Conclusion and discussion
In this study, we explored the generalization
capacity of a recently proposed neural network, ANN-MWI, for MWI. Our results demonstrate
the feasibility of the network on datasets with different resolution and noise
levels. On the other hand, the increased errors were reported for the datasets
with different RF shapes and TEs, suggesting the dependency of ANN-MWI on refocusing profile and TE. These errors might be reduced by fine-tuning the network with
a few data using transfer learning.9
For different disease types, ANN-MWI showed similar results in NMO data but an increased
error in edema data. The increased error might be caused by different T2
characteristics of the edema lesion. To decrease the errors, data augmentation
method10 can be utilized, which may expand the generalization
capacity of ANN-MWI.Acknowledgements
This research was supported by the Brain Korea 21 Plus Project in 2019 and the Brain Research Program through the
National Research Foundation of Korea (NRF) funded by the Korea government (MSIT)
(No. NRF-2017M3C7A1047864 and NRF-2018R1A2B3008445).References
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