Yiran Li1, Danfeng Xie1, Hanlu Yang1, Li Bai1, Guanshu Liu2, and Ze Wang3
1Department of Electrical and Computer Engineering, Temple University, PHILADELPHIA, PA, United States, 2Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
Synopsis
Chemical Exchange
Saturation Transfer (CEST) is an MR based imaging method that can image compounds
containing protons exhibiting a suitable exchange rate with bulk water. One of the
crucial technical hurdles in CEST MRI is, as CEST signal highly depends on the
saturation frequency, how to accurately correct the B0 inhomogeneity in each voxel. We
proposed two deep learning (DL) based methods for estimating B0 inhomogeneities
to accelerate CEST imaging using spare samples. While only a small sample size
was used, our study shows the potential of DL-based B0 mapping, which can
greatly reduce the total CEST acquisition time.
Introduction
Chemical Exchange
Saturation Transfer (CEST) MRI has emerged as a novel technology for precise diagnosis
of various diseases by either endogenous molecules such as mobile proteins1 and
glutamate2, or exogenous
administered contrast agents such as glucose and its derivatives3-5. One
of the crucial technical hurdles in CEST MRI is, as CEST signal highly depends
on the saturation frequency, how to accurately correct the B0 inhomogeneity in each voxel. While B0
inhomogeneity maps can be measured using phase mapping6 or Water Saturation Shift
Referencing (WASSR)7, these
methods require extra acquisition time and co-registration steps to match the
acquired B0 maps with CEST images. In
the present study, we hypothesized that sparely sampled Z-spectral images
indeed can be used to estimate B0 inhomogeneity maps by
the means of deep learning (DL), which will eliminate the needs for additional data
acquisition and processing and hence is more efficient.Methods
In
vivo MR studies were
carried out on a Biospec11.7 T horizontal MRI scanner equipped with a 23 mm
mouse brain volume coil. MR images were acquired dynamically after i.v.
injection of dextran1 (2 g/kg)
in C57BL6 mice (female, 5-6 weeks, n=5) bearing GL261 orthotopic brain tumors
at 21 days after stereotactically injecting 2x104 GL261 cells at a
depth of 3 mm below the dura. Two sets of steady-state Z-spectral CEST MR images (±3 ppm, step=0.2 ppm, total 31 offsets) were acquired before and
after the injection of dextran1 using a modified fat-suppressed RARE sequence
(CW saturation pulse, B1=1.8 µT and 3 seconds, TR/TE=5000/5 ms, RARE
factor=23, total acquisition time=7 min 45 sec). Pre and post-injection B0
inhomogeneities were also measured using the WASSR method7 using the
same RARE sequence (CW pulse , B1=0.5 µT and 500 ms, ±1 ppm,
step=0.1 ppm, TR/TE=1500/5 ms, RARE factor=23, total acquisition time=1
min 35 sec). Total of 8 sets of CEST images from 4 mice were used as training
data and 4 sets of CEST images from 2 mice were used as testing data. All
images were first masked to remove background voxels prior to DL.
As shown in Fig.1, Both voxel- and patch-based deep neural networks were investigated using data sets that were nonlinearly transformed from CEST-weighted images at 8 selected offsets (±100, ±200, ±300, -400, and -500Hz). The voxel-based model is based on fully connected deep neuro networks contains 3 hidden layers with 100 filters. For each voxel, the CEST image intensity of at the 8 offset frequencies was used as the input of neural network, and B0 offset is the output. The image patch-based model is composed of a deep residual network armed with wide activation layers. The backbone of the network is the vanilla residual network8 except the residual blocks were replaced by Wide-activation Deep Super-Resolution (WDSR) blocks9. Both methods were retrospectively evaluated using the B0 maps acquired by WASSR. The mean squared error was used as the lost function for both networks and adaptive moment estimation (ADAM) was used as the optimizer10. Results and Discussion
Fig.2(A)(B) shows representative training and testing results of the voxel- and patch-based model respectively. “Reference” was the results obtained by WASSR. “Prediction” shows the B0 offset maps estimated by the voxel- and patch-based models. “Absolute error” is the difference between the reference and the DL methods. Both methods produced B0 offset maps quite similar to the reference. Compared to the voxel-based method, the patch-based model resulted in more spatially constrained errors. Quantitatively, the voxel-based method showed slightly better performance than the patch-based one regarding the B0 map prediction errors and the structural similarity as shown in Table 1 and 2. This difference may be attributed to the small sample size. Even based on the same training data, the training sample size for the voxel-based method is much bigger than that for the patch-wise DL model simply because each voxel becomes a training sample. Moreover, the patch-based method has more parameters to be trained, which theoretically require more training samples. Since the patch-based method explicitly uses spatial correlation among the neighboring voxels, it can increase SNR of the output B0 map if more training samples will be available to refine the model. Nevertheless, the results from the two different DL models proved the effectiveness of DL for B0 map estimation.
Because our DL B0 mapping methods used spare samples, it can greatly accelerate CEST imaging. In this demonstration, the full Z-spectral acquisition took almost 8 min and the WASSR acquisition took ~ 1.5 min. Considering only 8 offsets are needed for DL, the total acquisition time is only ~ 2 min. Of course, one also needs to acquire CEST images at the offsets of interest (e.g.,1 ppm for dextran or glucose), making the total acquisition time 2-3 min. Hence, with our current settings, one can easily save up to 80% of the total acquisition time. Conclusion
We demonstrated the use of
two DL based methods for estimating B0 inhomogeneities to accelerate CEST
imaging using spare samples. While only a small sample size was used, our study
shows the potential of DL-based B0 mapping, which can greatly reduce the total CEST
acquisition time. Acknowledgements
This project was supported by the National
Institute of Biomedical Imaging and Bioengineering of the National Institute of
Health under award number p41EB015893 and the National Institute of Drug Abuse
of the National Institutes of Health under award number R01DA037289, and by
NIH/HIA R01AG060054-01 and by NIH R01CA211087.References
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