Kuo-Wei Lai1,2, Jeremias Sulam1, Manisha Aggarwal3, Peter van Zijl2,3, and Xu Li2,3
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, 2F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 3Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States
Synopsis
We
designed a method called Orientation-Grasp Deep Neural Network (OG-DNN) for
Quantitative Susceptibility Mapping (QSM). OG-DNN has dynamically adaptive
convolutional filters that adjust themselves according to the input B0 orientation in the subject
frame of reference. Our experimental results demonstrate that OG-DNN can
reconstruct high-quality and consistent susceptibility maps from MR phase data
acquired at different head orientations with respect to B0 within a consistent subject frame of reference. OG-DNN
is expected to provide improved flexibility in practice and may potentially
facilitate the development of deep learning-based Susceptibility Tensor Imaging
(STI) reconstructions.
Introduction
Quantitative Susceptibility Mapping (QSM) aims
at imaging the tissue magnetic susceptibility from MR phase measurements by solving
an ill-posed dipole
inversion problem.1, 2 Conventional methods either
utilize multiple phase measurements at different head
orientations to fill in the region with singularity in the dipole kernel, i.e.
the COSMOS method,3 or use regularized iterative methods to mitigate
the streaking artifacts induced by direct
dipole inversion using single orientation phase data.4-6 COSMOS QSM
exhibits excellent image quality, but
is expensive in terms of data acquisition, while single orientation iterative
methods usually need parameter tuning and may introduce extra smoothing effects
according to the regularization constrains. Recently,
deep learning-based QSM methods e.g. QSMnet7 and DeepQSM8
have shown the capability of performing QSM dipole inversion to
generate high-quality, e.g. COSMOS like, QSM reconstruction, using phase maps
from only a single orientation. However, all
these approaches are constrained to training the neural network model with a
fixed dipole kernel, i.e. with a fixed B0
orientation in the lab frame of reference, which limits the flexibility in
practical use. In this work, we present
an orientation-adaptive alternative which adapts to different dipole kernels corresponding to different orientations of the main magnetic
field in the subject frame.
This method achieves
comparable reconstruction performance compared to QSMnet.
In addition, this method may be useful in the potential design of deep
learning-based reconstruction with more complex models such as STI.9Methods
For training and testing data pairs, we collected
a total of 31 MR phase measurements acquired at 7T (Philips Achieva, 32 channel head coil) with two 3D
GRE sequences with 1 mm isotropic resolution on 7 healthy subjects (5
orientations for 3 subjects with TR=28 ms, TE1/ΔTE=5/5
ms, 5 echoes, 4 orientations for another 4 subjects with TR=45 ms, TE1/ΔTE=2/2 ms, 9 echoes). The study was IRB approved and informed consent
was obtained from all participants. The corresponding COSMOS were reconstructed for each subject after multiple steps of phase
preprocessing including best-path based phase unwrapping10 and
V-SHARP11 for removing the background and echo averaging for echoes
with TEs between 10 ms and 30 ms. We also
augmented our dataset by generating 31 simulated local
phase maps at random head orientations (within ±45°
to z-axis) using the 7 COSMOS. For this stage of our study, five subjects were used for training, and two subjects for validation. Full leave-one-out cross-validation will be
employed in follow-up work. Inspired
by studies on image super-resolution
and dynamic filter learning,12, 13 we included a dedicated adaptable module capable
of handling different B0
orientations in the subject frame, i.e. different dipole kernels, making the
overall architecture more applicable in practice. The proposed OG-DNN model
(Figure 1) contains a primary fully convolutional neural network and an Orientation-Grasp
Module. The primary fully convolutional neural network is composed of stacks of
wide-dropout residual blocks14 and takes local phase measurements as
input. The Orientation-Grasp Module, by taking
different B0 directions as
input, predicts varying weights of orientation-adaptive convolutional
filters after the primary neural network.
We trained our OG-DNN with an L1 loss and Stochastic Gradient Descent
(SGD) using 3D patch data with a size of 64×64×64 voxels. The training time is
approximately 48hr and the reconstruction time is 8s per image. We compared the results of our method with QSMnet and conventional methods including TKD4
and MEDI15 using the validation set in the next section. Results
Table
1 summarizes the QSM reconstruction
results on our validation set using different methods evaluated by quantitative
performance metrics, Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise
Ratio (PSNR), High Frequency Error Norm (HFEN), and Structural Similarity Index
(SSIM). COSMOS is treated as the gold standard reconstruction ground-truth for all
metric evaluations. These results show that our OG-DNN achieves a PSNR
of 39.24dB which is comparable to QSMnet performance. Figure 2 displays the three plane views of QSM maps from one subject in the validation set reconstructed using different methods.
The result of OG-DNN has significantly less artifacts and is also the closest
to COSMOS. Figure 3 displays
results of one validation example with input local phase acquired at 5
different head orientations using different methods, demonstrating that the
results from OG-DNN are consistent across different orientations.Discussion
Compared to current deep learning-based QSM
methods, our OG-DNN QSM method not only learns the dipole kernel inversion, but
also provides more flexibility in training and deployment. It can be directly
trained and applied on registered phase data in a consistent subject frame. On
the contrary, previous methods, e.g. such as QSMnet, assume a fixed B0 direction, e.g. in the z-axis, thus only learn the deconvolution of a fixed dipole kernel. Additionally,
the proposed OG-DNN should be tested with more diverse orientations with
large rotation angles to further demonstrate the capability of the Orientation-Grasp Module.Conclusion
We have developed
an OG-DNN model for QSM reconstruction, and demonstrated its capability of
adapting to different orientations of the main magnetic field in the subject frame of reference, thus different dipole
kernels. Such network design might facilitate future development of deep
learning-based STI which has to incorporate MR phase data acquired at
different head orientations to estimate tissue susceptibility tensors.Acknowledgements
Funding support: NCRR and NIBIB (P41 EB015909).References
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