Random forest regression for magnetic resonance image synthesis
Open accessAbstract
By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T 2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T 2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
A universal image quality index
Wang, Z., & Bovik, A. C. (2002) · IEEE Signal Processing Letters · doi:10.1109/97.995823 · Cited by 5,757 · see citing works
Current Methods in Medical Image Segmentation
Pham, D. L., Xu, C., & Prince, J. L. (2000) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.2.1.315 · Cited by 2,352 · see citing works
Unbiased average age-appropriate atlases for pediatric studies
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.033 · Cited by 2,290 · see citing works
Topology matching for fully automatic similarity estimation of 3D shapes
Hilaga, M., Shinagawa, Y., Kohmura, T., & Kunii, T. L. (2001) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/383259.383282 · Cited by 2,188 · see citing works
Unbiased nonlinear average age-appropriate brain templates from birth to adulthood
Fonov, V., Evans, A., McKinstry, R. C., Almli, C. R., & Collins, D. L. (2009) · NeuroImage · doi:10.1016/s1053-8119(09)70884-5 · Cited by 2,092 · see citing works
Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., & Salesin, D. (2001) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/383259.383295 · Cited by 1,579 · see citing works
Fast free-form deformation using graphics processing units
Marc Modat, A., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Sebastien Ourselin, A. (2009) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.002 · Cited by 1,087 · see citing works
Automated model-based tissue classification of MR images of the brain
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.811270 · Cited by 1,024 · see citing works
New variants of a method of MRI scale standardization
Nyúl, L. G., Udupa, J. K., & Zhang, X. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.836373 · Cited by 958 · see citing works
Reconstructing a 3D structure from serial histological sections
Sebastien Ourselin, A., Roche, A., Subsol, G., Pennec, X., & Ayache, N. (2001) · Image and Vision Computing · doi:10.1016/s0262-8856(00)00052-4 · Cited by 548 · see citing works
Mathematical textbook of deformable neuroanatomies.
Miller, M. I., Christensen, G. E., Amit, Y., & Grenander, U. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.24.11944 · Cited by 464 · see citing works
Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies
Burgos, N., Cardoso, M. J., Thielemans, K., Marc Modat, A., Pedemonte, S., Dickson, J. C., Barnes, A., Ahmed, R. M., Mahoney, C., Schott, J. M., et al. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2340135 · Cited by 365 · see citing works
A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions
Shiee, N., Bazin, P., Ozturk, A., Reich, D., Calabresi, P., & Pham, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.005 · Cited by 355 · see citing works
Longitudinal changes in cortical thickness associated with normal aging
Thambisetty, M., Wan, J., Carass, A., An, Y., Prince, J. L., & Resnick, S. M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.258 · Cited by 343 · see citing works
Multi-parametric neuroimaging reproducibility: A 3-T resource study
Landman, B. A., Huang, A. J., Gifford, A., Vikram, D. S., Lim, I. A. L., Farrell, J. A. D., Bogovic, J., Hua, J., Chen, M., Jarso, S., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.047 · Cited by 296 · see citing works
Characterizing white matter with magnetization transfer andT2
Stanisz, G. J., Kecojević, A., Bronskill, M. J., & Henkelman, R. M. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199912)42:6 · Cited by 249 · see citing works
Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J. C., Quiles, A., Valls, L., Ramió‐Torrentà, L., & Rovira, À. (2011) · Information Sciences · doi:10.1016/j.ins.2011.10.011 · Cited by 240 · see citing works
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images
Geremia, E., Olivier Clatz, A., Menze, B. H., Ender Konukoglu, A., Criminisi, A., & Ayache, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.080 · Cited by 232 · see citing works
CRUISE: Cortical reconstruction using implicit surface evolution
Han, X., Pham, D. L., Tosun, D., Rettmann, M. E., Xu, C., & Prince, J. L. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.043 · Cited by 232 · see citing works
A non-local approach for image super-resolution using intermodality priors☆
Rousseau, F. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.04.005 · Cited by 168 · see citing works
Multi-atlas segmentation with augmented features for cardiac MR images
Bai, W., Shi, W., Ledig, C., & Rueckert, D. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.09.005 · Cited by 159 · see citing works
Magnetic Resonance Image Example-Based Contrast Synthesis
Roy, S., Carass, A., & Prince, J. L. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2282126 · Cited by 146 · see citing works
Is Synthesizing MRI Contrast Useful for Inter-modality Analysis?
Iglesias, J. E., Konukoğlu, E., Zikic, D., Glocker, B., Leemput, K. V., & Fischl, B. R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_79 · Cited by 142 · see citing works
Hyperintensity in the Subarachnoid Space on FLAIR MRI
STUCKEY, S. L., Goh, T. D., Heffernan, T., & Rowan, D. (2007) · American Journal of Roentgenology · doi:10.2214/ajr.07.2424 · Cited by 142 · see citing works
Cross-Domain Synthesis of Medical Images Using Efficient Location-Sensitive Deep Network
Nguyen, H. V., Zhou, K., & Vemulapalli, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_83 · Cited by 130 · see citing works
Simple paradigm for extra-cerebral tissue removal: Algorithm and analysis
Carass, A., Cuzzocreo, J. L., Wheeler, M. B., Bazin, P., Resnick, S. M., & Prince, J. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.045 · Cited by 114 · see citing works
Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization
Ye, D. H., Zikic, D., Glocker, B., Criminisi, A., & Ender Konukoglu, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_76 · Cited by 108 · see citing works
Why Does Synthesized Data Improve Multi-sequence Classification?
van Tulder, G., & de Bruijne, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_65 · Cited by 89 · see citing works
Magnetic resonance image synthesis through patch regression
Jog, A., Roy, S., Carass, A., & Prince, J. L. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556484 · Cited by 84 · see citing works
Sebastien Ourselin, A., Sabuncu, M. R., Wells, W. M., Leo Joskowicz, B., Ünal, G., & Maier, A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.012 · Cited by 83 · see citing works
Encoding atlases by randomized classification forests for efficient multi-atlas label propagation
Zikic, D., Glocker, B., & Criminisi, A. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.010 · Cited by 81 · see citing works
A Compressed Sensing Approach for MR Tissue Contrast Synthesis
Roy, S., Carass, A., & Prince, J. L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22092-0_31 · Cited by 80 · see citing works
PET Attenuation Correction Using Synthetic CT from Ultrashort Echo-Time MR Imaging
Roy, S., Wang, W. T., Carass, A., Prince, J. L., Butman, J. A., & Pham, D. L. (2014) · Journal of Nuclear Medicine · doi:10.2967/jnumed.114.143958 · Cited by 79 · see citing works
MR image synthesis by contrast learning on neighborhood ensembles
Jog, A., Carass, A., Roy, S., Pham, D. L., & Prince, J. L. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.05.002 · Cited by 76 · see citing works
Rousseau, F. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_38 · Cited by 75 · see citing works
Context-sensitive Classication Forests for Segmentation of Brain Tumor Tissues
Zikic, D., Glocker, B., Ender Konukoglu, A., Shotton, J., Criminisi, A., Ye, D., Demiralp, Ç., Thomas, O., Das, T., Jena, R., et al. (2012) · Cited by 53 · see citing works
MR contrast synthesis for lesion segmentation
Roy, S., Carass, A., Shiee, N., Pham, D. L., & Prince, J. L. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490140 · Cited by 50 · see citing works
Information Processing in Medical Imaging
Sebastien Ourselin, A., Alexander, D. C., Westin, C., & Cardoso, M. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4 · Cited by 50 · see citing works
Improving magnetic resonance resolution with supervised learning
Jog, A., Carass, A., & Prince, J. L. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868038 · Cited by 46 · see citing works
Electrical Engineering (ICEE), 2010 18th Iranian Conference on
Mohammadkhani, R., & Thompson, J. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 42 · see citing works
Template-Based Multimodal Joint Generative Model of Brain Data
Cardoso, M. J., Sudre, C. H., Marc Modat, A., & Sebastien Ourselin, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_2 · Cited by 40 · see citing works
Reconstruction of the human cerebral cortex robust to white matter lesions: Method and validation
Shiee, N., Bazin, P., Cuzzocreo, J. L., Ye, C., Kishore, B., Carass, A., Calabresi, P. A., Reich, D. S., Prince, J. L., & Pham, D. L. (2013) · Human Brain Mapping · doi:10.1002/hbm.22409 · Cited by 38 · see citing works
Automatic Segmentation of Neonatal Brain MRI
Prastawa, M. W., Gilmore, J. H., Lin, W., & Gerig, G. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30135-6_2 · Cited by 35 · see citing works
Atlas Based Intensity Transformation of Brain MR Images
Roy, S., Jog, A., Carass, A., & Prince, J. L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02126-3_6 · Cited by 29 · see citing works
Consistent segmentation using a Rician classifier
Roy, S., Carass, A., Bazin, P., Resnick, S. M., & Prince, J. L. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.12.001 · Cited by 27 · see citing works
Example-Based Restoration of High-Resolution Magnetic Resonance Image Acquisitions
Ender Konukoglu, A., van der Kouwe, A. J. W., Sabuncu, M. R., & Fischl, B. R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_17 · Cited by 24 · see citing works
Synthesizing MR contrast and resolution through a patch matching technique
Roy, S., Carass, A., & Prince, J. L. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.844575 · Cited by 21 · see citing works
Random forest FLAIR reconstruction from T1, T2, and PD-weighted MRI
Jog, A., Carass, A., Pham, D. L., & Prince, J. L. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868061 · Cited by 18 · see citing works
Hierarchical Reconstruction of 7T-like Images from 3T MRI Using Multi-level CCA and Group Sparsity
Bahrami, K., Shi, F., Zong, X., Shin, H. W., An, H., & Shen, D. G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_79 · Cited by 18 · see citing works
Super-resolution MRI images using Compressive Sensing
Roohi, S., Zamani, J., Noorhosseini, M., & Rahmati, M. (2012) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2012.6292620 · Cited by 17 · see citing works
A supervised patch-based image reconstruction technique: Application to brain MRI super-resolution
Rousseau, F., & Studholme, C. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556483 · Cited by 13 · see citing works
Pulse sequence based multi-acquisition MR intensity normalization
Jog, A., Roy, S., Carass, A., & Prince, J. L. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2007062 · Cited by 12 · see citing works
Scandent Tree: A Random Forest Learning Method for Incomplete Multimodal Datasets
Hor, S., & Moradi, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_85 · Cited by 11 · see citing works
Tree-Encoded Conditional Random Fields for Image Synthesis
Jog, A., Carass, A., Pham, D. L., & Prince, J. L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_58 · Cited by 8 · see citing works
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- Bahrami Khosro Shi Feng Zong Xiaopeng Shin Hae Won An Hongyu Shen Dinggang 2015 Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 18th International Conference Munich, Germany October 5–9, 2015 Proceedings, Part II Springer International Publishing 659 666 PMC6085103 30101232
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012 · see citing works
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks
Dar, S. U. H., Yurt, M., Karacan, L., Erdem, A., Erdem, E., & Çukur, T. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2901750 · Cited by 614 · see citing works
ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis
Dalmaz, O., Yurt, M., & Çukur, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3167808 · Cited by 613 · see citing works
Unsupervised Medical Image Translation With Adversarial Diffusion Models
Özbey, M., Dalmaz, O., Dar, S. U. H., Bedel, H. A., Öztürk, Ş., Güngör, A., & Çukur, T. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3290149 · Cited by 542 · see citing works
Artificial intelligence and machine learning for medical imaging: A technology review
Montero, A. M. B., Javaid, U., Valdés, G., Nguyen, D. T. D., Desbordes, P., Macq, B. M. M., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.016 · Cited by 481 · see citing works
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge
Carass, A., Roy, S., Jog, A., Cuzzocreo, J. L., Magrath, E., Gherman, A., Button, J., Nguyen, J. V., Prados, F., Sudre, C. H., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.064 · Cited by 357 · see citing works
Multimodal MR Synthesis via Modality-Invariant Latent Representation
Chartsias, A., Joyce, T., Giuffrida, M. V., & Tsaftaris, S. A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2764326 · Cited by 343 · see citing works
Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis
Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., & Bourgeat, P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2895894 · Cited by 300 · see citing works
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Huo, Y., Xu, Z., Moon, H., Bao, S., Assad, A., Moyo, T. K., Savona, M. R., Abramson, R. G., & Landman, B. A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2876633 · Cited by 278 · see citing works
Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network
Sharma, A., & Hamarneh, G. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2945521 · Cited by 254 · see citing works
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes
Dewey, B. E., Zhao, C., Reinhold, J. C., Carass, A., Fitzgerald, K. C., Sotirchos, E. S., Saidha, S., Oh, J., Pham, D. L., Calabresi, P. A., et al. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.041 · Cited by 230 · see citing works
Evaluating the impact of intensity normalization on MR image synthesis
Reinhold, J. C., Dewey, B. E., Carass, A., & Prince, J. L. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2513089 · Cited by 218 · see citing works
Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data
Chartsias, A., Joyce, T., Dharmakumar, R., & Tsaftaris, S. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68127-6_1 · Cited by 194 · see citing works
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Hu, S., Lei, B., Wang, S., Wang, Y., Feng, Z., & Shen, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3107013 · Cited by 189 · see citing works
mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis
Yurt, M., Dar, S. U. H., Erdem, A., Erdem, E., Oguz, K. K., & Çukur, T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101944 · Cited by 177 · see citing works
Dar, S. U. H., Yurt, M., Shahdloo, M., Ildiz, M. E., Tınaz, B., & Çukur, T. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3001737 · Cited by 175 · see citing works
Yang, N., Ser, J. D., Walsh, S. L. F., Schönlieb, C., Roberts, M. T., SELBY, I. A., Howard, K., Owen, J., Neville, J., Guiot, J., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.01.001 · Cited by 152 · see citing works
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis
Wang, C., Yang, G., Papanastasiou, G., Tsaftaris, S. A., Newby, D. E., Gray, C. D., Macnaught, G., & MacGillivray, T. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.015 · Cited by 117 · see citing works
3D cGAN based cross-modality MR image synthesis for brain tumor segmentation
Yu, B., Zhou, L., Wang, L., Fripp, J., & Bourgeat, P. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363653 · Cited by 109 · see citing works
Wang, G., Song, T., Dong, Q., Cui, M., Huang, N., & Zhang, S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101787 · Cited by 105 · see citing works
Multimodal MRI synthesis using unified generative adversarial networks
Dai, X., Lei, Y., Fu, Y., Curran, W. J., Liu, T., Mao, H. Z., & Yang, X. (2020) · Medical Physics · doi:10.1002/mp.14539 · Cited by 95 · see citing works
Machine learning-enabled optimization of extrusion-based 3D printing
Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94 · see citing works
Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis
Cao, B., Zhang, H., Wang, N., Gao, X., & Shen, D. G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6619 · Cited by 92 · see citing works
Multi-modal mri image synthesis via gan with multi-scale gate mergence
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Yu, B., Wang, Y., Wang, L., Shen, D. G., & Zhou, L. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-33128-3_2 · Cited by 85 · see citing works
Huang, Y., Shao, L., & Frangi, A. F. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2781192 · Cited by 85 · see citing works
Islam, A. R. M. T., Talukdar, S., Mahato, S., Ziaul, S., Eibek, K. U., Akhter, S., Pham, Q. B., Mohammadi, B., Karimi, F., & Linh, N. T. T. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-12806-z · Cited by 82 · see citing works
Saha, T. K., Pal, S., Talukdar, S., Debanshi, S., Khatun, R., Singha, P., & Mandal, I. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.113344 · Cited by 80 · see citing works
Hu, B., Dai, Y., Zhou, H., Sun, Y., Yu, H., Dai, Y., Wang, M., Ergu, D., & Zhou, P. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134865 · Cited by 75 · see citing works
Robust Multi-modal MR Image Synthesis
Joyce, T., Chartsias, A., & Tsaftaris, S. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_40 · Cited by 72 · see citing works
Jiang, W., Xing, X., Li, S., Zhang, X., & Wang, W. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.12.093 · Cited by 72 · see citing works
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
Yang, X. Y., Lin, Y., Wang, Z., Li, X. S., & Cheng, K.-T. T. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2922986 · Cited by 70 · see citing works
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Zhang, Y., Peng, C., Wang, Q., Song, D., Li, K., & Zhou, S. K. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3424785 · Cited by 71 · see citing works
Self-consistent recursive diffusion bridge for medical image translation
Arslan, F., Kabaş, B., Dalmaz, O., Özbey, M., & Çukur, T. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103747 · Cited by 74 · see citing works
A literature survey of MR-based brain tumor segmentation with missing modalities
Zhou, T., Ruan, S., & Hu, H. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102167 · Cited by 66 · see citing works
Wang, Y., Chen, Y., Yang, N., Zheng, L., Dey, N., Ashour, A. S., Rajinikanth, V., Tavares, J. M. R. S., & Shi, F. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.10.006 · Cited by 65 · see citing works
Multimodal MR Image Synthesis Using Gradient Prior and Adversarial Learning
Liu, X., Yu, A., Wei, X., Pan, Z., & Tang, J. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3013418 · Cited by 63 · see citing works
Memory-efficient GAN-based domain translation of high resolution 3D medical images
Uzunova, H., Ehrhardt, J., & Heinz Handels, A. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101801 · Cited by 61 · see citing works
Cross contrast multi-channel image registration using image synthesis for MR brain images
Chen, M., Carass, A., Jog, A., Lee, J., Roy, S., & Prince, J. L. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.10.005 · Cited by 58 · see citing works
Artificial intelligence in glioma imaging: Challenges and advances
Jin, W., Fatehi, M., Abhishek, K., Mallya, M., Toyota, B., & Hamarneh, G. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8131 · Cited by 55 · see citing works
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
Dalmaz, O., Mirza, M. U., Elmas, G., Özbey, M., Dar, S. U. H., Ceyani, E., Oğuz, K. K., Avestimehr, S., & Çukur, T. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103121 · Cited by 54 · see citing works
Brain lesion segmentation through image synthesis and outlier detection
Bowles, C. T., Qin, C., Guerrero, R., Gunn, R. N., Hammers, A., Dickie, D. A., Hernández, M. V., Wardlaw, J. M., & Rueckert, D. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.09.003 · Cited by 50 · see citing works
Deep learning for the harmonization of structural MRI scans: a survey
Abbasi, S., Lan, H.-Y., Choupan, J., Sheikh-Bahaei, N., Pandey, G., & Varghese, B. A. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01280-6 · Cited by 49 · see citing works
PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers
Zhang, X., He, X.-Z., Guo, J., Ettehadi, N., Aw, N., Semanek, D., Posner, J., Laine, A., & Wang, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3174827 · Cited by 50 · see citing works
Yang, Y., Zeng, Q., Yin, G., & Wan, L. (2019) · IEEE Access · doi:10.1109/access.2019.2932118 · Cited by 46 · see citing works
AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis
Cao, B., Bi, Z., Hu, Q., Zhang, H., Wang, N., Gao, X., & Shen, D. G. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01791-0 · Cited by 45 · see citing works
Edge-preserving MRI image synthesis via adversarial network with iterative multi-scale fusion
Luo, Y., Nie, D., Zhan, B., Li, Z., Wu, X., Zhou, J., Wang, Y., & Shen, D. G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.060 · Cited by 44 · see citing works
Yang, H., Sun, J. Z., & Xu, Z. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3301934 · Cited by 40 · see citing works
Huang, P., Li, D., Jiao, Z., Wei, D., Cao, B., Mo, Z., Wang, Q., Zhang, H., & Shen, D. G. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102472 · Cited by 40 · see citing works
Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis
Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., & Bourgeat, P. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2969630 · Cited by 39 · see citing works
Learning multi-site harmonization of magnetic resonance images without traveling human phantoms
Liu, S., & Yap, P. (2024) · Communications Engineering · doi:10.1038/s44172-023-00140-w · Cited by 38 · see citing works
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
Mehta, R., Christinck, T., Nair, T., Bussy, A., Premasiri, S., Costantino, M., Chakravarthy, M. M., Arnold, D. L., Gal, Y., & Arbel, T. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3114097 · Cited by 38 · see citing works
Regulatory learning: How to supervise machine learning models? An application to credit scoring
Guégan, D., & Hassani, B. K. (2018) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2018.04.001 · Cited by 36 · see citing works
Yurt, M., Özbey, M., Dar, S., Tınaz, B., Oğuz, K., & Cukur, T. (2020) · Medical Image Anal. · doi:10.1016/j.media.2022.102429 · Cited by 40 · see citing works
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
Maheshwari, H., Liu, Y.-C., & Kira, Z. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00106 · Cited by 37 · see citing works
Wu, J., Guo, D., Wang, L., Yang, S., Zheng, Y., Shapey, J., Vercauteren, T. K., Bisdas, S., Bradford, R., Saeed, S. R., et al. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126295 · Cited by 35 · see citing works
Jiang, W., Xing, X., Zhang, X., & Mi, M. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.08.089 · Cited by 34 · see citing works
Deep learning‐based multi‐modal computing with feature disentanglement for MRI image synthesis
Fei, Y., Zhan, B., Hong, M., Wu, X., Zhou, J., & Wang, Y. (2021) · Medical Physics · doi:10.1002/mp.14929 · Cited by 34 · see citing works
Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images
Qin, Z., Liu, Z., Zhu, P., & Ling, W. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105928 · Cited by 34 · see citing works
Zhan, B., Li, D., Wang, Y., Ma, Z., Wu, X., Zhou, J., & Zhou, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102457 · Cited by 30 · see citing works
Reducing Gadolinium Contrast With Artificial Intelligence
Tsui, B. Q., Calabrese, E., Zaharchuk, G., & Rauschecker, A. M. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29095 · Cited by 29 · see citing works
Inan, M. S. K., Sworna, N. S., Islam, A. K. M. M., Islam, S., Alom, Z., Azim, M. A., & Shatabda, S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105773 · Cited by 30 · see citing works
Kwak, M. G., Mao, L., Zheng, Z., Su, Y., Lure, F. Y. M., Li, J., & Initiative, A. D. N. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3556290 · Cited by 31 · see citing works
Deep learning classification of lipid droplets in quantitative phase images
Sheneman, L., Stephanopoulos, G. N., & Vasdekis, A. E. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249196 · Cited by 29 · see citing works
Autoencoder-Based Collaborative Attention GAN for Multi-Modal Image Synthesis
Cao, B., Cao, H., Liu, J., Zhu, P., Zhang, C., & Hu, Q. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3274990 · Cited by 29 · see citing works
Machine Learning Applications for Computer-Aided Medical Diagnostics
Oza, P., Sharma, P., & Patel, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0733-2_26 · Cited by 28 · see citing works
A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation
Yang, H., Sun, J. Z., Yang, L., & Xu, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_12 · Cited by 27 · see citing works
D2FE-GAN: Decoupled dual feature extraction based GAN for MRI image synthesis
Zhan, B., Zhou, L., Li, Z., Wu, X., Pu, Y., Zhou, J., Wang, Y., & Shen, D. G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109362 · Cited by 27 · see citing works
Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs
Guo, P., Wang, P., Yasarla, R., Zhou, J., Patel, V. M., & Jiang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3046460 · Cited by 25 · see citing works
Detection of Foreign-Body in Milk Powder Processing Based on Terahertz Imaging and Spectrum
Hu, J., Xu, Z., Li, M., He, Y., Sun, X., & Liu, Y. (2021) · Journal of Infrared Millimeter and Terahertz Waves · doi:10.1007/s10762-021-00802-w · Cited by 25 · see citing works
Recent Advances in Dental Panoramic X-Ray Synthesis and Its Clinical Applications
Anusree, P. S., Parida, B. K., & You, W. (2024) · IEEE Access · doi:10.1109/access.2024.3422650 · Cited by 24 · see citing works
Mehta, R., & Arbel, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00536-8_13 · Cited by 24 · see citing works
MIL normalization —— prerequisites for accurate MRI radiomics analysis
Hu, Z., Zhuang, Q., Xiao, Y., Wu, G., Shi, Z., Chen, L., Wang, Y., & Yu, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104403 · Cited by 23 · see citing works
Model of Gradient Boosting Random Forest Prediction
Zhang, Z., Zhu, X., & Liu, D. (2022) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc55942.2022.10004112 · Cited by 22 · see citing works
Cross-modality Neuroimage Synthesis: A Survey
Xie, G., Huang, Y., Wang, J., Lyu, J., Zheng, F., Zheng, Y., & Jin, Y. (2023) · ACM Computing Surveys · doi:10.1145/3625227 · Cited by 21 · see citing works
Attention Based Cross-Domain Synthesis and Segmentation From Unpaired Medical Images
Liu, X., Pan, J., Li, X., Wei, X., Liu, Z., Pan, Z., & Tang, J. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3296499 · Cited by 21 · see citing works
Cross‐Model Transformer Method for Medical Image Synthesis
Hu, Z., Liu, H., Li, Z., & Yu, Z. (2021) · Complexity · doi:10.1155/2021/5624909 · Cited by 21 · see citing works
PTNet: A High-Resolution Infant MRI Synthesizer Based on TransformerPreprint
Zhang, X., He, X.-Z., Guo, J., Ettehadi, N., Aw, N., Semanek, D., Posner, J., Laine, A., & Wang, Y. (2021) · arXiv.org · doi:10.48550/arxiv.2105.13993 · Cited by 39 · see citing works
Random forests in medical image computing
Konukoğlu, E., & Glocker, B. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00024-7 · Cited by 20 · see citing works
Kim, S., Jang, H., Hong, S., Hong, Y. S., Bae, W. C., Kim, S. J., & Hwang, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102198 · Cited by 20 · see citing works
Validation and evaluation metrics for medical and biomedical image synthesis
Nečasová, T., Burgos, N., & Svoboda, D. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824349-7.00032-3 · Cited by 19 · see citing works
Diao, Y.-Q., Li, F., & Li, Z. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107234 · Cited by 17 · see citing works
Energy-guided diffusion model for CBCT-to-CT synthesis
Fu, L., Li, X., Cai, X., Miao, D., Yu, Y., & Shen, Y. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102344 · Cited by 19 · see citing works
Hermann, I., Martínez‐Heras, E., Rieger, B., Schmidt, R., Golla, A., Hong, J., Lee, W., Yu‐Te, W., Nagtegaal, M. A., Solana, E., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28688 · Cited by 19 · see citing works
Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation
Dewey, B. E., Zhao, C., Carass, A., Oh, J., Calabresi, P. A., van Zijl, P. C. M., & Prince, J. L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00536-8_3 · Cited by 18 · see citing works
Wu, W., Wang, Y., Tang, J., Yu, M. Z., Yuan, J., & Zhang, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107328 · Cited by 19 · see citing works
Feng, Y., Deng, S., Lyu, J., Cai, J., Wei, M.-Q., & Qin, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3445969 · Cited by 20 · see citing works
Nour, M., Sindi, H. F., Abozinadah, E., Öztürk, Ş., & Polat, K. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104300 · Cited by 17 · see citing works
Wu, K., Qiang, Y., Song, K., Ren, X., Yang, W., Zhang, W., Hussain, A., & Cui, Y. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01507-7 · Cited by 17 · see citing works
A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis
Zhao, C., Carass, A., Lee, J., Jog, A., & Prince, J. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68127-6_4 · Cited by 16 · see citing works
Liu, X., Zhang, Z., Jiang, T., Li, X., & Li, Y. (2021) · Journal of Spectroscopy · doi:10.1155/2021/5957376 · Cited by 16 · see citing works
Deep‐learned short tau inversion recovery imaging using multi‐contrast MR images
Kim, S., Jang, H., Jang, J., Lee, Y. H., & Hwang, D. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28327 · Cited by 16 · see citing works
A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation
Wang, Y., Hu, H., Yu, S., Yang, Y., Guo, Y., Song, X., Chen, F., & Liu, Q. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acdc80 · Cited by 15 · see citing works
Addressing Missing Modality Challenges in MRI Images: A Comprehensive Review
Azad, R., Dehghanmanshadi, M., Khosravi, N., Cohen‐Adad, J., & Merhof, D. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450399 · Cited by 14 · see citing works
Xia, Y., Ravikumar, N., Lassila, T., & Frangi, A. F. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102814 · Cited by 14 · see citing works
Moezzi, S. M. M., Mohammadi, M., Mohammadi, M., Saloğlu, D., & Sheikholeslami, R. (2024) · Environmental Monitoring and Assessment · doi:10.1007/s10661-024-12567-5 · Cited by 14 · see citing works
Density Functional Theory and Machine Learning of Transition Metals in Mo2C for Gas Sensors
Huang, W., Dong, Z., & Lin, L. (2024) · ACS Applied Nano Materials · doi:10.1021/acsanm.4c04274 · Cited by 13 · see citing works
Nair, M., Bica, I., Best, S. M., & Cameron, R. E. (2021) · Applied Physics Reviews · doi:10.1063/5.0059724 · Cited by 12 · see citing works
Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis
Valencia, L., Clèrigues, A., Valverde, S., Salem, M., Oliver, A., Rovira, À., & Lladó, X. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.954662 · Cited by 12 · see citing works
- Authors
- Jog, A., Carass, A., Roy, S., Pham, D. L., & Prince, J. L.
- Affiliations
- Johns Hopkins University (US)
- Henry M. Jackson Foundation (US)
- Corresponding author
- Jog, Amod
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 35
- Pages
- 475-488
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC5099106
- OpenAlex
- W2517395172
- Cited by
- 212
- References
- 64
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 29 · 2023: 25 · 2022: 20 · 2021: 39 · 2020: 25 · 2019: 14 · 2018: 14 · 2017: 9 · 2016: 1
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Medical Image Analysis (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (R21 EB012765)
- NIBIB (R01 EB017743)
- NINDS (R01 NS070906)
- Department of Defense
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- MRI image synthesis, random forest regression, replicas, tissue contrast, pulse sequences
- MeSH terms
- Algorithms, Humans, Image Enhancement, Magnetic Resonance Imaging, Datasets as Topic
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