Joseph H. C. Lai1, Jianpan Huang1, Xiongqi Han1, Jiadi Xu2,3, and Kannie W. Y. Chan1,2
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, 2Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States
Synopsis
There is an
urgent need to develop an efficient and noninvasive methods to diagnose AD at
an early stage. Artificial neural network (ANN) is a powerful
model for prediction and classification of diseases, thus, it has been applied to
facilitate prognosis and diagnosis. We propose to apply ANN based on chemical exchange saturation
transfer (CEST) MRI at 3T to detect AD. Our phantom and AD mouse
results showed that the trained ANN was able to identify AD from age-matched
wild-type (WT) mice with high accuracy, which could provide valuable
information for AD diagnosis.
Introduction
Stratification
of Alzheimer’s disease (AD), which is a common form of dementia, has played an
important role to guide treatments, especially in the aging populations. Many
disease-modifying treatments are available to slow-down disease progression,
which are effective when AD is diagnosed at an early stage (1,2). Hence, there
is an urgent need to develop an efficient and noninvasive methods to diagnose early-stage
AD. Chemical
exchange saturation transfer (CEST) MRI is an imaging technique, which
can detect low-concentration metabolites with exchangeable protons in vivo, such
as protein, glucose or lipid (3-10). The so-called Z-spectrum containing these molecular information is used to
characterize CEST contrast (5,11,12). We have demonstrated that CEST MRI can detect the
protein aggregation or altered glucose uptake in AD mouse brain (13,14). However, these unique contrasts could overlap when
Z-spectrum is acquired at low fields (e.g. ≤3T), resulting in subtle differences between AD and
wild type (WT) mice. ANN
is a powerful approach that can assist prediction and classification
when features are obscure (15-18).
Here, we proposed to apply ANN based on z-spectra to identify AD mice from WT mice.
To mimic pathological changes in protein levels, we examined the feasibility of
using ANN to classify different concentrations of bovine serum albumin (BSA) in vitro. Then we applied the ANN to identify
Z-spectra of AD mouse brains from that of WT mouse brains based on CEST MRI at
3T.Methods
A phantom containing three 2-mL tubes was designed to mimic
the protein concentrations (13,19).
All components were kept constant except the BSA concentrations (Table 1, 5%, 10% and 15% for three tubes
respectively). The components were dissolved in PBS at pH=7.2. Three AD mice (APP/PS1,
16M, male, Jackson Laboratory, Maine) and three age-matched WT mice (16M,
male) were used in this study. All MRI experiments
were performed on a horizontal bore 3T Bruker BioSpec system (Bruker, Germany).
CEST sequence was a continuous wave (CW) saturation module followed by a Rapid
Acquisition with Relaxation Enhancement (RARE) sequence as a read-out module. The
imaging parameters were as follows: repetition time (TR)=8000ms; echo time (TE)=5.8ms;
field of view (FOV)=20×20mm2; matrix size=96×96; slice thickness=2mm; slice number=3; RARE factor=32; saturation power (B1)= 0.8μT and
saturation time (tsat)=2s. Frequency
offsets of Z-spectrum were uniformly
distributed between ±8ppm with 0.2ppm step size (81 points) and four M0
images at 200ppm were acquired, resulting in a total scanning time of 34min for
each set. The ANN is a fully connected neural network with two hidden layers (120
and 84 neurons, respectively), as shown in Fig. 1, where the input is a full
Z-spectrum and the output is a N×1 vector containing the probabilities for categories (N=3 for phantom
and N=2 for mouse brain here). Dropout with keep probability of 0.9 was applied
in the two hidden layers to prevent ANN from overfitting (20). The activation function in
hidden layers was ReLU (21) and the optimized algorithm
was Adam (22). The training and testing
data size are shown in Table 2. Batch size was set to 512, and number of epochs
was 50 for phantom and 5000 for mouse brain, thus leading to a training time of
10s and 417s for phantom and mouse brain respectively. Training and testing were
performed on MATLAB R2018b (Mathworks, Natick, MA) on a personal computer
(Intel Core i7, 16 GB memories). Results and Discussion
Phantom
was scanned six times (five for training and one for testing) in independent
MRI experiments. Representative Z-spectra of phantoms are shown in Fig. 2B,
differences were observed at different protein levels, especially at ±3.5ppm which represents the APT and NOE. Therefore, the ANN can be fast trained to
reach a promising classification accuracy and an extremely low training loss
(Fig. 2C). The training accuracy is 99.95% and the testing accuracy is 97.64%,
as shown in Table 3. The prediction map generated by the trained ANN clearly
showed the classification of three tubes, especially the tube with 5% BSA (Fig.
2D). There were some pixels in the boundary of tubes with 10% and 15% BSA were not
well predicted, mainly due to the partial volume effect. For the mouse brain,
the Z-spectra did not have obvious differences between AD and WT mice (Fig. 3A), which lead to a larger
variation in the training accuracy and loss curves (Fig. 3B). Nevertheless, the
ANN was still well trained to a right trend, with a training accuracy of 87.43%
and test accuracy of 78.54% at last (Table 3). The prediction maps composed of
probability of each pixels also showed an apparent identification of AD from WT
(Fig. 3D&E).Conclusion
We applied an ANN based on Z-spectrum of CEST MRI to
detect AD at 3T. Phantom results showed that the trained ANN can achieve a
testing accuracy of 97.64% for classifying phantoms with different BSA concentrations.
Similarly, ANN can identify Z-spectra of AD mouse
brains from age-matched WT mouse brains with a training accuracy of 87.43% and
testing accuracy of 78.54%. This approach could provide valuable information for
the AD diagnosis.
#J.H.C. Lai and J. Huang contributed equally.Acknowledgements
We are grateful to receive funding support from the Research Grants Council (RGC) of Hong Kong [9042620] and the City University of Hong Kong [9610362, 7004859, 7005210].References
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