Publications about MRI simulation
Recommended reading:
H.A. Nieuwstadt et al., Numerical Simulations of Carotid MRI Quantify the Accuracy in Measuring Atherosclerotic Plaque Components In Vivo, Magn Reson Med 72:188–201 (2014)
H.A. Nieuwstadt et al., A Computer-Simulation Study on the Effects of MRI Voxel Dimensions on Carotid Plaque Lipid-Core and Fibrous Cap Segmentation and Stress Modeling, PLOS ONE | DOI:10.1371 / journal.pone.0123031 April 9, 2015 (open access)
T. Stoecker et al., High-performance computing MRI simulations. Magn Reson Med 64:186–193 (2010) (see also the JEMRIS website)
More info on MRI simulation can be found here
Conference publications by the openGTN project
ISMRM Benelux conference, 24 January 2020, Arnhem, The Netherlands
Aymen Ayaz et al., Pipeline for simulating realistic anatomically variable normal young, aging and diseased brain MRI (abstract | poster)
Sina Amirrajab et al., Towards generating realistic and heterogeneous cardiac MR simulated image database for deep-learning based segmentation (abstract | poster)
Yasmina Al Khalil et al., Improving the generalization capability of deep-learning based algorithms for ventricular cavity segmentation using simulated CMR images (abstract)
Medical Imaging with Deep Learning (MIDL) conference, 6-8 July 2020, online meeting
Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer , 4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model (oral), available on Openreview and arXiv
International Society of Magnetic Resonance in Medicine (ISMRM),
28th Annual Meeting, 8-13 August 2020, online meeting
Sina Amirrajab et al., Towards Realistic Cardiac MR Image Simulation; Inclusion of the Endocardial Trabeculae in the XCAT Heart Anatomy (abstract 2207) (poster)
Sina Amirrajab et al., Generation of realistic and heterogeneous virtual population of cardiovascular magnetic resonance simulated images (abstract 2209) (poster)
Aymen Ayaz et al., Realistic MRI simulation pipeline for anatomically variable normal young, aging and diseased brain (abstract 1871) (poster)
Yasmina Al Khalil et al., Simulated CMR images can improve the performance and generalization capability of deep learning-based segmentation algorithms (abstract 3519) (poster)
Yasmina Al Khalil et al., Addressing the need for less MRI sequence dependent DL-based segmentation methods: model generalization to multi-site and multi-scanner data (abstract 3560) (poster)
VPH 2020 Conference, 24-28 August, Paris, France
E. Kruithof, M.J.M. Cluitmans, K.D. Lau, M. Breeuwer, Influence of Image Artifacts on Outcome of Simulated Cardiac Electrophysiology, Book of Abstracts. p. 83-84
European Society of Magnetic Resonance in Medicine and Biology (ESMRMB), September 30 – October 2, 2020 (online meeting)
Yasmina Al Khalil et al., Automated multimodal segmentation of paraspinal muscles based on chemical shift encoding-based water/fat-separated images (abstract) (poster)
Sina Amirrajab et al., A Multipurpose Numerical Simulation Tool for Late Gadolinium Enhancement Cardiac MR Imaging (abstract) (poster)
Aymen Ayaz et al. Simulating realistic appearing multiple contrast brain MRI (abstract) (poster)
Evianne Kruithof, Sina Amirrajab et al., Influence of Image Artifacts on Image-Based Electrophysiological Simulations Using Simulated XCAT Phantom MR Images (abstract) (poster)
Medical Image Computing and Compter-Assisted Interventions (MICCAI) conference, 4-8 October 2020 (online meeting)
Amirrajab, et al. “XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms” (oral) (poster) (paper)
SASHIMI 2020, a MICCAI 2020 Workshop, Al Khalil, Y. Amirrajab, S. et al. “Heterogeneous Virtual Population of Simulated CMR Images for Improving the Generalization of Cardiac Segmentation Algorithms” (oral) (paper) (poster)
M&Ms-2 Challenge at MICCAI 2021, 27 September – 1 October 2021, Strasbourg, France (online meeting)
Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation” (oral)(paper)
International Society of Magnetic Resonance in Medicine (ISMRM),
29th Annual Meeting, 7-12 May 2022, London, United Kingdom (hybrid meeting)
S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “Intra- and intersubject synthesis of cardiac MR images using a VAE and GAN” (abstract)(poster)
S. Amirrajab, Y. Al Khalil, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “sim2real: Cardiac MR image simulation-to-real translation via unsupervised GANs” (abstract)(poster)
Y. Al Khalil, A. Ayaz, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “Reducing the impact of texture on deep-learning brain tissue segmentation networks trained with simulated MR images” (abstract)(poster)
Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer, “Late feature fusion and GAN-based augmentation for generalizable cardiac MRI segmentation” (abstract)(poster)
A. Ayaz, K. Lukassen, C. Lorenz, J. Weese, M. Breeuwer. “Brain MR image super resolution using simulated data to perform in real-world MRI” (abstract)(poster)
A. Ayaz, R. Jong, S. Abbasi-Sureshjani, S. Amirrajab, C. Lorenz, J. Weese, M. Breeuwer. “3D brain MRI synthesis utilizing 2D SPADE-GAN and 3D CNN architecture” (abstract)(poster)
Medical Imaging Computing and Computer Assisted Interventions (MICCAI) conference, Singapore, 18-22 September 2022
Y. Al Khalil, A. Ayaz, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer, “A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data”, Data Augmentation, Labelling, and Imperfections: Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Proceedings. Nguyen, H. V., Huang, S. X. & Xue, Y. (eds.). Springer, p. 92-101 10 p. (Lecture Notes in Computer Science (LNCS); vol. 13567) (paper)
S. Amirrajab, Y. Al Khalil, J. Pluim, M. Breeuwer, C.M. Scannell, “Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data”, Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers: 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Camara, O., Puyol-Antón, E., Suinesiaputra, A., Young, A., Qin, C., Sermesant, M. & Wang, S. (eds.). Cham: Springer, p. 466-475 10 p. (Lecture Notes in Computer Science (LNCS); vol. 13593) (paper)
Journal publications by the openGTN project
E. Kruithof, S. Amirrajab, M.J.M. Cluitmans, K.D. Lau, M. Breeuwer. “Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology”. Computers in Biology and Medicine 137 (2021): 104773. (paper)
E. Kruithof, S. Amirrajab, K.D. Lau, M. Breeuwer. “Simulated late gadolinium enhanced cardiac magnetic resonance imaging dataset from mechanical XCAT phantom including a myocardial infarct.” Data in Brief (2021): 107691. (paper)
Lustermans, D. R. P. R. M., Amirrajab, S., Veta, M., Breeuwer, M. & Scannell, C. M., “Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation”, 27 Sep 2021, In: arXiv. 2109, 12940 (paper)
Lustermans, D. R. P. R. M., Amirrajab, S., Veta, M., Breeuwer, M. & Scannell, C. M., “Optimized automated cardiac MR scar quantification with GAN-based data augmentation”, 1 Nov 2022, In: Computer Methods and Programs in Biomedicine. 226, 9 p., 107116 (paper)
Amirrajab, S., Khalil, Y. A., Lorenz, C., Weese, J., Pluim, J. & Breeuwer, M., “sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs”, 9 Aug 2022, In: arXiv. 2022, 6 p., 2208.04874 (paper)
Amirrajab, S., Lorenz, C., Weese, J., Pluim, J. & Breeuwer, M., “Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs”, Sep 2022, Simulation and Synthesis in Medical Imaging : 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. Springer, p. 34-42 11 p. (Lecture Notes in Computer Science (LNCS); vol. 13570) (paper)
Amirrajab, S., Al Khalil, Y., Lorenz, C., Weese, J., Pluim, J. & Breeuwer, M., “Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks”, 1 Oct 2022, In: Computerized Medical Imaging and Graphics. 101, 14 p., 102123 (paper)
Khalil, Y. A., Becherucci, E. A., Kirschke, J. S., Karampinos, D. C., Breeuwer, M., Baum, T. & Sollmann, N., “Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database”, 23 Mar 2022, In: Scientific Data. 9, 11 p., 97 (paper)
Al Khalil, Y., Amirrajab, S., Pluim, J. & Breeuwer, M., 2022, “Late Fusion U-Net with GAN-Based Augmentation for Generalizable Cardiac MRI Segmentation”, Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge: 12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Revised Selected Papers. Puyol Antón, E., Pop, M., Martín-Isla, C., Sermesant, M., Suinesiaputra, A., Camara, O., Lekadir, K. & Young, A. (eds.). Cham: Springer, p. 360-373 14 p. (Lecture Notes in Computer Science (LNCS); vol. 13131)(Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP); vol. 13131) (paper)
S. Amirrajab, Y. Al Khalil, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer, “A Framework for Simulating Cardiac MR Images with Varying Anatomy and Contrast”, IEEE Transactions on Medical Imaging. 42, 3, p. 726-738 13 p., 9924194 (paper)
Martin-Isla, C., Campello, V. M., Izquierdo, C., Kushibar, K., Sendra-Balcells, C., Gkontra, P., Sojoudi, A., Fulton, M. J., Arega, T. W., Punithakumar, K., Li, L., Sun, X., Al Khalil, Y., Liu, D., Jabbar, S., Queiros, S., Galati, F., Mazher, M., Gao, Z., Beetz, M., & 20 others, “Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge”, 17 Apr 2023, (E-pub ahead of print) In: IEEE Journal of Biomedical and Health Informatics (paper)
Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer, “On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images”, Medical Image Analysis. 84, 15 p., 102688 (paper)
S. Amirrajab, Y. Al Khalil, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer, “Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks”, Computerized Medical Imaging and Graphics. 101, 14 p., 102123 (paper)
PhD thesis
S. Amirrajab, “Simulation and Synthesis for Cardiac Magnetic Resonance Imaeg Analysis”, PhD thesis Eindhoven University of Technology, defended 20 April 2023 (download) (news)
