Beomgu Kang1, Byungjai Kim1,2, Michael Schar2, Hyunwook Park1, and Hye Young Heo2,3
1Department of Electrical Engineeering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkin University, Baltimore, MD, United States, 3F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
Synopsis
Most currently used MTC/CEST imaging
protocols depend on the acquisition of qualitative weighted images, limiting
the detection sensitivity to quantitative parameters, their exchange rate and
concentration. Here, we propose a fast, quantitative 3D MTC/CEST imaging
framework based on a combined 1) time-interleaved parallel RF transmission, 2) compressed sensing, 3) MR fingerprinting, and 4) deep-learning techniques. Typically,
supervised deep learning requires a massive amount of labeled images for
training, which is limited particularly in MTC/CEST MRI field. However, the
proposed unsupervised learning architecture requires only small amounts of
unlabeled MTC/CEST data.
Introduction
Applications of conventional CEST have focused
mostly on the use of MTR asymmetry (MTRasym) analysis1-2.
However, CEST signals measured by MTRasym analysis are unavoidably
contaminated by upfield nuclear Overhauser enhancement (NOE) and asymmetric MTC
signals, thus limiting the assessment of pure CEST effects3-5.
Therefore, accurate MTC quantification at a certain CEST frequency is essential
in such a subtraction-based method. The most promising MTC quantification methods
currently required fitting of MTC signals acquired from repeated and serial
imaging acquisition with varied saturation powers and frequency offsets using
the solution of the Bloch equation6-8. However, the fitting method
is sometimes challenging to deploy in clinical practice due to the high
computational cost and the difficulty of fitting parameter selection. In this
study, we developed a fast, quantitative MTC/CEST imaging technique combined with
1) time-interleaved parallel RF transmission (pTX), 2) MR fingerprinting (MRF), 3) compressed
sensing (CS), and 4) unsupervised deep-learning techniques. Typically,
supervised deep learning requires a huge amount of labeled training data, which
is impractical in MTC/CEST MRI field. Instead, we focused on fully-unsupervised
learning from limited amounts of data without prior knowledge (label-free). Methods
In the 3D MRF framework (Fig. 1), RF
saturation times (Ts), powers (B1), frequency offsets (Ω), and relaxation delay
times (Td) were pseudo-randomly applied throughout the acquisition9-11,
generating unique MTC signal evolutions for different tissue properties.
Continuous RF saturation (100% duty cycle) was achieved using the pTX
technique, which can achieve highly sensitive saturation effects on clinical
scanners and increase the degree of freedom for Ts. For CS acceleration
(4-fold), the variable-density sampling with an elliptical centric k-space
ordering pattern was used. For in-vivo studies, seven healthy volunteers
(repeated three times on the same day) were scanned on a 3T MRI scanner after
informed consent was obtained in accordance with the IRB requirement. In order
to predict quantitative MTC parameters from MRF images, the unsupervised deep
learning scheme was designed to train a convolutional neural network (CNN)
architecture (Fig. 2). The label-free MTC-MRF images (‘Input’ in Fig. 2) were
fed to the architecture, which outputs initial MTC (exchange rate: kmw,
concentration: M0m, T2 relaxation: T2m)
and water T1 (T1w) parameter maps. Next,
MTC-MRF images (‘Output’ in Fig. 2) were synthetically generated by solving a
two-pool Bloch equation using the initial MTC parameters, MRF scan information,
and additionally acquired water T2 (T2w) map.
The CNN weights were trained to extract features from the input images which
predict MTC parameters by minimizing the mean square difference (loss function)
between the original MTC-MRF and synthesized MTC-MRF images. Amide proton
transfer (APT) and NOE images with RF saturation power of 1.2 μT were
calculated by subtracting synthesized MTC images (Zref) from B0-corrected,
experimentally measured images (Zlab) at ±3.5 ppm, respectively (Z =
Ssat/S0, where Ssat and S0 are the signal
intensities measured with and without RF saturation, respectively). Results and Discussion
Excellent agreement was observed for the
original (input) and synthesized MTC-MRF (output) images with 40 dynamic scans
by the unsupervised deep learning as shown in Fig. 3. The average
root-mean-square-error (RMSE) and structural similarity (SSIM) values were
0.026 and 0.985, respectively. The estimation of the MTC parameters satisfied all
the 40 RF saturation conditions that would guarantee unique solution of the
ill-posed inverse Bloch equation problem. Quantitative MTC parameter maps
obtained from the unsupervised deep learning and conventional Bloch equation
fitting methods are shown in Fig. 4 (center slice of a total of 9 image slices
shown). The MTC parameter values estimated by unsupervised deep learning method
were in excellent agreement with values estimated by using conventional Bloch
fitting approach, but dramatically reduced computation time (100 sec vs. 3 hrs
for 4D images: 256 x 256 x 9 x 40). In addition, the estimated parameters were
in good agreement with previous observations6,7. Synthesized MTC at
3.5 ppm, downfield APT, and upfield NOE images are shown in Fig. 5. Broad
signals from faster exchanging protons, such as amine and guanidinium protons,
can make contributions at the APT frequency12-14. However, slower
exchanging protons dominate at low B1 and faster ones dominate at
high B1 because the saturation efficiency depends on the exchange
rate. Therefore, slow-exchanging amide protons are the main contribution at 3.5
ppm under B1 of 1.2 μT.Conclusions
We developed a highly sensitive (via pTX),
accelerated (via CS), and quantitative (via MRF and deep learning) 3D MTC
imaging technique and applied to CEST and NOE imaging. The proposed deep
learning architecture was designed to solve inverse problem of the MTC Bloch
equation. While conventional CNN-based methods require massive labeled (for
ground truth) datasets which are time-consuming and labor-intensive, the
proposed unsupervised deep learning architecture incorporated with the Bloch
equation-based model learns a set of unique feature that can describe the
MTC-MRF input, and allows only small amounts of unlabeled data for learning.
Given that the considerable time efficiency compared to conventional Bloch
fitting, unsupervised deep learning-based MRF could be a powerful tool for quantitative
MTC/CEST imaging. Acknowledgements
No acknowledgement found.References
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