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Research ArticleArtificial Intelligence

Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging

Catalina Raymond, Jingwen Yao, Bryan Clifford, Thorsten Feiweier, Sonoko Oshima, Donatello Telesca, Xiaodong Zhong, Heiko Meyer, Richard G. Everson, Noriko Salamon, Timothy F. Cloughesy and Benjamin M. Ellingson
American Journal of Neuroradiology April 2025, 46 (4) 733-741; DOI: https://doi.org/10.3174/ajnr.A8566
Catalina Raymond
aFrom the UCLA Brain Tumor Imaging Laboratory (C.R., J.Y., S.O., B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
bDepartment of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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Jingwen Yao
aFrom the UCLA Brain Tumor Imaging Laboratory (C.R., J.Y., S.O., B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
bDepartment of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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Bryan Clifford
cSiemens Medical Solutions USA, Inc. (B.C.), Los Angeles, CA
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Thorsten Feiweier
dSiemens Healthineers AG (T.F., H.M.), Erlangen, Germany
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Sonoko Oshima
aFrom the UCLA Brain Tumor Imaging Laboratory (C.R., J.Y., S.O., B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
bDepartment of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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Donatello Telesca
iDepartment of Biostatistics (D.T.), University of California, Los Angeles, Los Angeles, CA, USA
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Xiaodong Zhong
bDepartment of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
hDepartment of Bioengineering (X.Z., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
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Heiko Meyer
dSiemens Healthineers AG (T.F., H.M.), Erlangen, Germany
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Richard G. Everson
eDepartment of Neurosurgery (R.G.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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Noriko Salamon
bDepartment of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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Timothy F. Cloughesy
fDepartment of Neurology (T.F.C.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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Benjamin M. Ellingson
aFrom the UCLA Brain Tumor Imaging Laboratory (C.R., J.Y., S.O., B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
bDepartment of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
gDepartment of Psychiatry and Biobehavioral Sciences (B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
hDepartment of Bioengineering (X.Z., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
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Abstract

BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high acceleration that is notoriously susceptible to artifacts.

MATERIALS AND METHODS: A total of 4,573 anatomical MR sequences from 1,392 patients undergoing clinically indicated MRI of the brain were used to create a synthetic data set using physics-based, simulated artifacts commonly found in EPI. By using multiple MRI contrasts, we hypothesized the GAN would learn to correct common artifacts while preserving the inherent contrast information, even for contrasts the network has not been trained on. A modified Pix2PixGAN architecture with an Attention-R2UNet generator was used for the model. Three training strategies were employed: (1) An “all-in-one” model trained on all the artifacts at once; (2) a set of “single models”, one for each artifact; and a (3) “stacked transfer learning” approach where a model is first trained on one artifact set, then this learning is transferred to a new model and the process is repeated for the next artifact set. Lastly, the “Stacked Transfer Learning” model was tested on ADC maps from single-shot diffusion MRI data in N = 49 patients diagnosed with recurrent glioblastoma to compare visual quality and lesion measurements between the natively acquired images and AI-corrected images.

RESULTS: The “stacked transfer learning” approach had superior artifact reduction performance compared to the other approaches as measured by Mean Squared Error (MSE = 0.0016), Structural Similarity Index (SSIM = 0.92), multiscale SSIM (MS-SSIM = 0.92), peak signal-to-noise ratio (PSNR = 28.10), and Hausdorff distance (HAUS = 4.08mm), suggesting that leveraging pre-trained knowledge and sequentially training on each artifact is the best approach this application. In recurrent glioblastoma, significantly higher visual quality was observed in model predicted images compared to native images, while quantitative measurements within the tumor regions remained consistent with non-corrected images.

CONCLUSIONS: The current study demonstrates the feasibility of using a physics-based method for synthesizing a large data set of images with realistic artifacts and the effectiveness of utilizing this synthetic data set in a “stacked transfer learning” approach to training a GAN for reduction of EPI-based artifacts.

ABBREVIATIONS:

BTIP
brain tumor imaging protocol
GAN
generative adversarial network
HAUS
Hausdorff distance
MS-SSIM
multiscale structural similarity index
MSE
mean square error
NAWM
Normal Appearing White Matter
PSNR
peak signal to noise ratio
RANO
Response Assessment in Neuro Oncology
RAS
Right, Anterior, Superior
SSIM
structural similarity index
  • © 2025 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 46 (4)
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Vol. 46, Issue 4
1 Apr 2025
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Cite this article
Catalina Raymond, Jingwen Yao, Bryan Clifford, Thorsten Feiweier, Sonoko Oshima, Donatello Telesca, Xiaodong Zhong, Heiko Meyer, Richard G. Everson, Noriko Salamon, Timothy F. Cloughesy, Benjamin M. Ellingson
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging
American Journal of Neuroradiology Apr 2025, 46 (4) 733-741; DOI: 10.3174/ajnr.A8566

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Deep Learning for Artifact Reduction in EPI
Catalina Raymond, Jingwen Yao, Bryan Clifford, Thorsten Feiweier, Sonoko Oshima, Donatello Telesca, Xiaodong Zhong, Heiko Meyer, Richard G. Everson, Noriko Salamon, Timothy F. Cloughesy, Benjamin M. Ellingson
American Journal of Neuroradiology Apr 2025, 46 (4) 733-741; DOI: 10.3174/ajnr.A8566
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