Highlights
- •The Generative Adversarial Network to reduce motion artifacts on liver MRI.
- •Training with the perceptual loss to preserve anatomical structure details.
- •Multiple types and degrees of motion artifacts simulation in image and k-space.
- •Good generalization of the model evaluated on liver MRI images with real motion.
Abstract
Abbreviations:
DCE-MRI (dynamic contrast enhanced magnetic resonance imaging), DRN-DCMB (deep residual network with densely connected multi-resolution block), CS (compressed sensing), CNNs (convolutional neural networks), DL (deep learning), 3D (three-dimensional), FFT (Fast Fourier Transform), BN (batch normalization), LeakyReLU (Leaky rectified linear unit), MSE (mean square error), SSIM (structural similarity index), TR (repetition time), TE (echo time), FA (flip angle), MARC (motion artifact reduction with convolution), GAN (generative adversarial network)Keywords
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- MRI of focal liver lesions.Curr Med Imaging Rev. 2012 May; 8: 107-116
- Assessment of liver fibrosis using pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging.J Magn Reson Imaging. 2016 Jul; 44: 98-104
- MRI arterial enhancement fraction in hepatic fibrosis and cirrhosis.Am J Roentgenol. 2013; 201: W596-W602
- Dynamic Contrast-enhanced MR imaging of advanced hepatocellular carcinoma: comparison with the liver parenchyma and correlation with the survival of patients receiving systemic therapy.Radiology. 2016; 281: 454-464
Balafar M. Review of noise reducing algorithms for brain MRI images. methods. 10:11.
- Reducing clinical MRI motion degradation using a prescan patient information pamphlet.Am J Roentgenol. 2013 Mar; 200: 630-634
- Clinical performance of contrast enhanced abdominal pediatric MRI with fast combined parallel imaging compressed sensing reconstruction.J Magn Reson Imaging. 2014; 40: 13-25
- Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction.Phys Med Biol. 2015; 60: R297-R322
- Free-breathing contrast-enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling.Investig. Radiol. 2013; 48: 10-16
- Improved pediatric MR imaging with compressed sensing.Radiology. 2010 Aug; 256: 607-616
- Basic of machine learning and deep learning in imaging for medical physicists.Physica Medica: Eur J Med Phys. 2021; 1: 194-205
Gjesteby L, Yang Q, Xi Y, Zhou Y, Zhang J, Wang G. Deep learning methods to guide CT image reconstruction and reduce metal artifacts. In: Medical Imaging 2017: Physics of Medical Imaging [Internet]. International Society for Optics and Photonics; 2017 [cited 2021 Jan 6]. p. 101322W.
- A deep cascade of convolutional neural networks for dynamic MR image reconstruction.IEEE Trans Med Imaging. 2018; 37: 491-503
Oksuz I, Clough J, Ruijsink B, Puyol-Antón E, Bustin A, Cruz G, et al. Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, et al., editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 [Internet]. Cham: Springer International Publishing; 2019 [cited 2021 Jan 6]. p. 695–703. (Lecture Notes in Computer Science; vol. 11767).
- Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks.J Magn Reson Imaging. 2019; 50: 1260-1267
- Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.Hum Brain Mapp. 2019; 40: 1290-1297
- Real-time control of respiratory motion: Beyond radiation therapy.Physica Medica: Eur J Medi Phys. 2019; 1: 104-112
- Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.Physica Medica: Eur J Med Phys. 2021; 1: 175-191
- Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).Magn Reson Imaging. 2020; 1: 69-79
- MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks.Magn Reson Med. 2019; 82: 2133-2145
Jiang W, Liu Z, Lee K-H, Chen S, Ng Y-L, Dou Q, et al. Respiratory motion correction in abdominal MRI using a densely connected U-Net with GAN-guided training. arXiv preprint arXiv:190609745. 2019;.
- Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver.Magn Reson Med Sci. 2019; 19: 64-76
- Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network.Eur Radiol. 2020; 30: 5923-5932
Jiang W, Liu Z, Lee K-H, Chen S, Ng Y-L, Dou Q, et al. Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training. :8.
Yang Q, Yan P, Kalra MK, Wang G. CT Image Denoising with Perceptive Deep Neural Networks. arXiv:170207019 [cs] [Internet]. 2017 Feb 22 [cited 2021 Jan 3];.
- Reproduction of motion artifacts for performance analysis of prospective motion correction in MRI.Magn Reson Med. 2014; 71: 182-190
- Motion artefacts in MRI: a complex problem with many partial solutions.J Magn Reson Imaging. 2015; 42: 887-901
Johnson J, Alahi A, Fei-Fei L. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. arXiv:160308155 [cs] [Internet]. 2016 Mar 26 [cited 2021 Jan 6];.
Xu B, Wang N, Chen T, Li M. Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv:150500853 [cs, stat] [Internet]. 2015 Nov 27 [cited 2021 Jan 6];.
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Internet]. Las Vegas, NV, USA: IEEE; 2016 [cited 2021 Jan 6]. p. 770–8.
- Photo-realistic single image super-resolution using a generative adversarial network.in: In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681-4690
Smith LB, Heise D. Perceptual Similarity and Conceptual Structure. In: Advances in Psychology [Internet]. Elsevier; 1992 [cited 2021 Jan 6]. p. 233–72.
- Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:14091556 [cs].[Internet]. 2015; (Apr 10 [cited 2021 Jan 6])
- ImageNet classification with deep convolutional neural networks.Commun ACM. 2017; 60: 84-90
Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014;.
- Image quality assessment: from error visibility to structural similarity.IEEE Trans Image Process. 2004 Apr; 13: 600-612
- Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests.Journal of Medical Engineering. 2017 Jun; 11: 1-9
- k-Space weighted image contrast (KWIC) for contrast manipulation in projection reconstruction MRI.Magn Reson Med. 2000; 44: 825-832
Yang Q, Yan P, Kalra MK, Wang G. CT Image Denoising with Perceptive Deep Neural Networks. arXiv:170207019 [cs] [Internet]. 2017 Feb 22 [cited 2021 Jan 6];.
Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv:160904802 [cs, stat] [Internet]. 2017 May 25 [cited 2021 Jan 17];.
- Feature extraction and image processing for computer vision.Academic press, 2019
- The unreasonable effectiveness of deep features as a perceptual metric.in: In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 586-595
- Probing shallower: perceptual loss trained Phase Extraction Neural Network (PLT-PhENN) for artifact-free reconstruction at low photon budget.Opt Express. 2020; 28: 2511
