Ho-Joon Lee1, Yeonah Kang1, Marc Lebel2, Jae Eun Song3, and Sung-Min Gho4
1Department of Radiology, Haeundae Paik Hospital, Busan, Republic of Korea, 2MR Collaboration and Development, GE Healthcare, Calagary, AB, Canada, 3Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea, 4MR Collaboration and Development, GE Healthcare, Seoul, Republic of Korea
Synopsis
With advances in deep learning, feasibility
has been investigated for myelin water fraction (MWF) reconstruction showing promising results,
enabling fast reconstruction,
however whether images denoised with deep learned reconstruction(DL) will
improve MWF map quality has not been investigated. After denoising of multi-echo GRE magnitude images with a DL algorithm, data was fitted to a three-component magnitude model. Use of DL, shows better results as compared to
conventionally generated maps (i.e. decreased NRMSE and mean fitting errors
(WM), increased PSNR and SSIM). Gibb's ringing artifact was removed remarkably.
Introduction
Gradient echo myelin water imaging
(GRE-MWI) is an MRI technique used to assess the amount of myelin (fraction of
myelin bound water, to be precise), in the brain.
With advances in deep learning, feasibility
has been investigated for MWF reconstruction showing promising results,
enabling fast reconstruction1,2,
however, whether images denoised with deep learned reconstruction (DL) will
improve MWF map quality has not been investigated.
For multi-exponential relaxometry,
including myelin water imaging, relatively high SNR is required for reasonable
results3,4, which require long scan times, the sacrifice of spatial resolution or
both.
Although, 3D acquisitions are usually used
for the acquisition of GRE-MWI, a 2D multi-slice acquisition-based approach was
recently proposed, which is shown to provide high-quality myelin water fraction
(MWF) maps, which are also insensitive to flip angle and B1 field inhomogeneity5.
However, 2D acquisition may suffer from lower signal-to-noise ratio (SNR)
compared to 3D acquisitions.
In this preliminary work, we sought to
evaluate whether the quality of 2D GRE-based myelin water imaging can be improved
using magnitude images denoised with DL as input.Methods
[Data
Acquisition]
Images were acquired in a volunteer, on a
3T MRI scanner (Signa Architect, GE Healthcare, Waukesha, WI, USA), with a 48-channel head coil.
mGRE (multi-echo spoiled gradient echo)
images were acquired with the following parameters: number of slices = 14 (3 mm thickness, 1.5 mm interslice gap); matrix
size = 128
x 128; field of view = 210 × 210 mm; repetition time = 85.4
ms; number of echoes 16; first TE = 1.7 ms; echo spacing 2.1 ms; bandwidth = 1562.5
Hz/Px; flip angle = 15° and total scan time = 2:43. A saturation module was placed
inferior to the imaging plane. A monopolar readout scheme was used.
[Image Reconstruction]
DLRecon is a new deep learning-based MR
reconstruction, which comprises a deep convolutional residual encoder network
trained using a database of over 10,000 images to achieve images with high SNR
and high spatial resolution
The acquired data were retrospectively reconstructed
with and without DLRecon at denoising levels of 50% (DL50), 70% (DL70), 90%
(DL90), and 100% (DL100) respectively.
[Three-component
magnitude model for MWI]
The three-component magnitude model fits the acquired T2* decay curve
to the following:
$$s(t)=(A_{my} e^{-(1/T_{2,my}^*)t}+A_{ax} e^{-(1/T_{2,ax}^* )t}+A_{ex} e^{-(1/T_{2,ex}^*)t})$$ where $$$A_{my}$$$ and $$$A_{ax}$$$ are the amplitude of the three water
components, $$$T_{2,my}^*$$$, $$$T_{2,ax}^*$$$, and $$$T_{2,ex}^*$$$ are $$$T_2^*$$$ values of the three water components6,7. The fitting
parameters are estimated by solving an iterative nonlinear curve-fitting
algorithm. The initial values and bounds of the fitting parameters were set the
same as in a previous work by Lee et al.8.
[Quantitative
analysis]
For the quantitative evaluation of DL performance, normalized root mean
squared error (NRMSE), peak signal-to-noise ratio (PSNR), and structural
similarity index (SSIM) were estimated based on the DL 100 magnitude image.
Also, least-squares error between acquired signal and the fitted signal was
estimated, especially the white matter regions (WM).Results
Use of DL, shows better results as
compared to conventionally generated maps (i.e. decreased NRMSE and mean
fitting errors (WM), increased PSNR and SSIM, with increasing denoising levels). One reason for the improvement of MWF results through DL application is due to the reduction of the fitting error by the SNR improvement. Measured mean least square fitting error values in the WM region of Figure 2 are 51.85 (Original), 46.52 (DL 50), 43.03 (DL 70), 40.09 (DL 90), and 38.78 (DL100), respectively (Figure 3.). Remarkably, Gibb’s ringing artifacts are virtually eliminated in
images reconstructed with deep learned algorithms, and resulting MWF maps, regardless
of the denoising level, in contrast to prominent artifacts visible with the
original reconstruction (Figure 2, 4.).
Discussion and Conclusion
The
preliminary results presented in this work based on three-component magnitude model
fitting of images reconstructed with a prototype software shows the potential of DL
for improving the quality of MWF maps. Further improvement is expected if combined
with other methods such as the use of navigator echoes9, complex fitting10, or compensation of eddy current
effects5.
DL
is known to perform well for removal of statistical and reconstruction related
noise, and artifacts such as Gibb’s ringing. However, motion artifacts or
physiologic noise cannot be removed and sometimes even be amplified. In
combination with recently proposed PCA based methods, either in the complex or
magnitude domain, these limitations may be resolved11,12. Acknowledgements
No acknowledgement found.References
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