Ho-Joon Lee1, Yeonah Kang1, Marc Lebel2, Joon-Hyeong Kim3, Dong-Hyun Kim3, 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 MREPT reconstruction showing interesting results. However whether images denoised with deep learned reconstruction will improve EPT map quality has not been investigated. After denoising of complex data acquired with a DL algorithm, EPT maps were generated with phase based 2D-weighted polynomial fitting. Use of DL, shows better results as compared to conventionally generated maps (i.e. decreased NRMSE, increased PSNR and SSIM, with increasing denoising levels), and results in sharper appearing maps. Spreading of boundary artifacts are not observed with increasing denoising factors.
Introduction
Magnetic resonance-based electrical properties tomography (MREPT), is a rapidly evolving field, with clinical translation mostly applied in the oncologic field, such as breast cancer1,2, brain tumor3, to name a few.
With advances in deep learning, feasibility has been investigated for MREPT reconstruction showing interesting results4,5, however, whether images denoised with deep learned reconstruction will improve image quality has not been investigated. Since signal to noise ratio (SNR) of the radiofrequency transmit field (B1+) is the most dominant factor for MREPT reconstruction, sufficient SNR needs to be reached at the time of acquisition6. Therefore, we hypothesized that deep learned reconstruction based denoising (DL) for MREPT may improve the quality of conductivity maps. Methods
[Data
Acquisition]
Images were acquired on a healthy
volunteer, with a 3T MRI scanner (Signa Architect, GE Healthcare, Waukesha, WI, USA), with a quadrature
birdcage transmit-receive coil.
The
required phase for conductivity reconstruction was calculated with complex data
from multi-slice T2‐weighted fast spin echo
(FSE) images that were acquired with the following parameters: number of slices = 3 (4 mm thickness, 0.4 mm interslice gap); matrix size = 192 × 192 or 256 x 256; field of view = 256 × 256 mm; repetition time = 4500 ms; effective TE = 102 ms; Echo Train Length = 20; and total scan time = 0:50 or 1:08 min,
respectively per number of excitation (NEX). 1, and 4 NEX images were acquired
for each matrix size.
[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 for each matrix size and number of
excitations.
[EPT reconstruction]
Among phase-based EPT algorithm7, a 2D-weighted polynomial fitting was performed to
reconstruct conductivity8 (maximum kernel size = 2.1 x 2.1 cm2). For the magnitude weighting factor, the magnitude intensity of the image was used.
[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.Results
Use of DL, shows better results as
compared to conventionally generated maps (i.e. decreased NRMSE, increased PSNR
and SSIM, with increasing denoising levels).
Although using DL shows better
results in terms of conductivity reconstruction than not, differences according
to denoising levels are not apparent. One reason is that the effect on DL
denoising is weakened because spatial filtering is already used in the
conductivity recon process (Figure 2., Figure 3.).
Increasing the conventional
filtering size results in the spreading of boundary artifacts, which is fatal
for estimating the conductivity of the tissue. However, this does not happen
even if the denoising factor was increased when using DL. Also, The brain structures appear sharper, probably owing to
the capability of DL to achieve high spatial resolution (Figure 2.).
The
effect of signal averaging or matrix size is less than the effect of
application of DL, however the benefit of increased SNR
with signal averaging can be observed on 4 NEX acquisition with conventional
reconstruction (Figure 2., Figure 3.).Discussion and Conclusion
The
preliminary results presented in this work based on images acquired on a
quadrature coil, and reconstruction based on a prototype software, shows the potential of DL for improving MREPT results. Since the innate difference in electrical property in the normal brain structures is
smaller than that observed between normal tissue and tumorous conditions,
application to tumor imaging may further demonstrate the benefit of the current
approach. Application to multi-channel-coil based methods8 is also
warranted.Acknowledgements
We thank Jin Young Park, and Ki Bok Choi for their assistance in image acquisition.References
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