7T-guided super-resolution of 3T MRI
Open accessAbstract
PURPOSE: High-resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super-resolution 3T MRI that resembles 7T MRI, which is called as 7T-like MR image in this paper. METHODS: First, we propose a mapping from 3T MRI to 7T MRI space, using regression random forest. The mapped 3T MR images serve as intermediate results with similar appearance as 7T MR images. Second, we predict the final higher resolution 7T-like MR images based on sparse representation, using paired local dictionaries for both the mapped 3T MR images and 7T MR images. RESULTS: Based on 15 subjects with both 3T and 7T MR images, the predicted 7T-like MR images by our method can best match the ground-truth 7T MR images, compared to other methods. Meanwhile, the experiment on brain tissue segmentation shows that our 7T-like MR images lead to the highest accuracy in the segmentation of WM, GM, and CSF brain tissues, compared to segmentations of 3T MR images as well as the reconstructed 7T-like MR images by other methods. CONCLUSIONS: We propose a novel method for prediction of high-resolution 7T-like MR images from low-resolution 3T MR images. Our predicted 7T-like MR images demonstrate better spatial resolution compared to 3T MR images, as well as prediction results by other comparison methods. Such high-quality 7T-like MR images could better facilitate disease diagnosis and intervention.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Super-resolution for MRI can be used to improve the trade-off between resolution, SNR, and acquisition time, generate 7T-like MR images on 3T MRI scanners, or obtain super-resolution T1 maps from a set of low resolution T1 weighted images.
“Super-resolution for MRI have been around for almost 10 years [192, 193] and can be used to improve the trade-off between resolution, SNR, and acquisition time [194], generate 7T-like MR images on 3T MRI scanners [195], or obtain super-resolution T 1 maps from a set of low resolution T 1 weighted images [196].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [195]
Super-resolution for MRI can be used to improve the trade-off between resolution, SNR, and acquisition time, generate 7T-like MR images on 3T MRI scanners, or obtain super-resolution T1 maps from a set of low resolution T1 weighted images.
“Super-resolution for MRI have been around for almost 10 years [192, 193] and can be used to improve the trade-off between resolution, SNR, and acquisition time [194], generate 7T-like MR images on 3T MRI scanners [195], or obtain super-resolution T 1 maps from a set of low resolution T 1 weighted images [196].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [194]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628 · see citing works
Image Super-Resolution Via Sparse Representation
Yang, J., Wright, J. N., Huang, T. S., & Ma, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2050625 · Cited by 5,241 · see citing works
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731 · see citing works
Enhancement of MR Images Using Registration for Signal Averaging
Holmes, C. J., Hoge, R. D., Collins, D. L., Woods, R. P., Toga, A. W., & Evans, A. C. (1998) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199803000-00032 · Cited by 1,422 · 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
Coupled Dictionary Training for Image Super-Resolution
Yang, J., Wang, Z., Lin, Z., Cohen, S., & Huang, T. S. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2192127 · Cited by 786 · see citing works
Fast and accurate image upscaling with super-resolution forests
Schulter, S., Leistner, C., & Bischof, H. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299003 · Cited by 633 · see citing works
Super-Resolution in Medical Imaging
Greenspan, H. (2008) · The Computer Journal · doi:10.1093/comjnl/bxm075 · Cited by 584 · see citing works
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
Peleg, T., & Elad, M. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2305844 · Cited by 406 · see citing works
Automated segmentation of hippocampal subfields from ultra‐high resolution in vivo MRI
Van Leemput, K., Bakkour, A., Benner, T., Wiggins, G., Wald, L. L., Augustinack, J., Dickerson, B. C., Golland, P., & Fischl, B. (2009) · Hippocampus · doi:10.1002/hipo.20615 · Cited by 405 · see citing works
LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations
Shi, F., Cheng, J., Wang, L., Yap, P., & Shen, D. G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2437894 · Cited by 317 · see citing works
Self-citationImage Super-Resolution With Sparse Neighbor Embedding
Gao, X., Zhang, K., Tao, D., & Li, X. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2190080 · Cited by 315 · see citing works
MRI inter-slice reconstruction using super-resolution
Greenspan, H. K., Oz, G., Kiryati, N., & Peled, S. (2002) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(02)00511-8 · Cited by 295 · see citing works
Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model
Huynh, T., Gao, Y., Kang, J., Wang, L., Zhang, P., Lian, J., & Shen, D. G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2461533 · Cited by 283 · see citing works
Self-citationPlenge, E., Poot, D. H. J., Bernsen, M. R., Kotek, G., Houston, J. G., Wielopolski, P. A., van der Weerd, L., Niessen, W. J., & Meijering, E. H. W. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24187 · Cited by 273 · see citing works
Clinical applications of 7T MRI in the brain
van der Kolk, A. G., Hendrikse, J., Zwanenburg, J. J. M., Visser, F., & Luijten, P. R. (2011) · European Journal of Radiology · doi:10.1016/j.ejrad.2011.07.007 · Cited by 264 · see citing works
Neonatal brain image segmentation in longitudinal MRI studies
Shi, F., Yong Fan, A., Songyuan, T., Gilmore, J. H., Lin, W., & Shen, D. G. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.07.066 · Cited by 197 · see citing works
Self-citationLearning Multiple Linear Mappings for Efficient Single Image Super-Resolution
Zhang, K., Tao, D., Gao, X., Li, X., & Xiong, Z. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2389629 · Cited by 195 · see citing works
Subfields of the hippocampal formation at 7 T MRI: In vivo volumetric assessment
Wisse, L. E. M., Gerritsen, L., Zwanenburg, J. J. M., Kuijf, H. J., Luijten, P. R., Biessels, G. J., & Geerlings, M. I. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.023 · Cited by 183 · see citing works
Intersection Based Motion Correction of Multislice MRI for 3-D in Utero Fetal Brain Image Formation
Kim, K., Habas, P. A., Rousseau, F., Glenn, O. A., Barkovich, A. J., & Studholme, C. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2030679 · Cited by 171 · see citing works
Single-image super-resolution of brain MR images using overcomplete dictionaries
Rueda, A., Malpica, N., & Romero, E. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.09.003 · Cited by 169 · see citing works
Jiang, J., Hu, R., Wang, Z., & Han, Z. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2347201 · Cited by 160 · 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
New methods of MR image intensity standardization via generalized scale
Madabhushi, A., & Udupa, J. K. (2006) · Medical Physics · doi:10.1118/1.2335487 · Cited by 138 · see citing works
MRI Superresolution Using Self-Similarity and Image Priors
Manjón, J., Coupé, P., Buades, A., Collins, D., & Robles, M. (2010) · International Journal of Biomedical Imaging · doi:10.1155/2010/425891 · Cited by 133 · 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
Clinical fMRI: Evidence for a 7T benefit over 3T
Beisteiner, R., Robinson, S., Wurnig, M. C., Hilbert, M., Merksa, K., Rath, J., Hoellinger, I., Klinger, N., Marosi, C., Trattnig, S., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.010 · Cited by 128 · see citing works
A Self-Learning Approach to Single Image Super-Resolution
Yang, M.-C., & Wang, Y.-C. F. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2232646 · Cited by 125 · see citing works
Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI
Alexander, D. C., Zikic, D., Zhang, J., Zhang, H. G., & Criminisi, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_29 · Cited by 113 · see citing works
Shilling, R. Z., Robbie, T. Q., Bailloeul, T., Mewes, K., Mersereau, R. M., & Brummer, M. E. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2007348 · Cited by 113 · 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
Carmi, E., Liu, S., Alon, N., Fiat, A., & Fiat, D. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2005.09.011 · Cited by 106 · see citing works
Collaborative patch-based super-resolution for diffusion-weighted images
Pierrick Coupé, A., Manjón, J. V., Chamberland, M., Descoteaux, M., & Hiba, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.030 · Cited by 100 · see citing works
Reconstruction of 7T-Like Images From 3T MRI
Bahrami, K., Shi, F., Zong, X., Shin, H. W., An, H., & Shen, D. G. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2549918 · Cited by 85 · see citing works
Self-citationMagnetic 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
The use of super‐resolution techniques to reduce slice thickness in functional MRI
Peeters, R. R., Kornprobst, P., Nikolova, M., Sunaert, S. G., Viéville, T., Malandain, G., Rachid Deriche, B., Faugeras, O. D., Ng, M. K., & Hecke, P. V. (2004) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.20016 · Cited by 75 · see citing works
Unsupervised Cross-Modal Synthesis of Subject-Specific Scans
Vemulapalli, R., Nguyen, H. V., & Zhou, S. K. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.79 · Cited by 69 · see citing works
Shared k‐space echo planar imaging with keyhole
Zaitsev, M., Zilles, K., & Shah, N. J. (2001) · Magnetic Resonance in Medicine · doi:10.1002/1522-2594(200101)45:1 · Cited by 67 · 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
Voxel-based morphometry at ultra-high fields. A comparison of 7T and 3T MRI data
Seiger, R., Hahn, A., Hummer, A., Kranz, G. S., Ganger, S., Küblböck, M., Kraus, C., Sladky, R., Kasper, S., Windischberger, C., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.019 · Cited by 48 · see citing works
WEBB, A. G., Collins, C. M., Versluis, M. J., Kan, H. E., & Smith, N. B. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22262 · Cited by 41 · see citing works
Normalization of brain magnetic resonance images using histogram even-order derivative analysis
Christensen, J. D. (2003) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(03)00102-4 · Cited by 41 · see citing works
Zhang, Y., Guorong Wu, A., Yap, P., Feng, Q., Lian, J., Chen, W., & Shen, D. G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2202245 · Cited by 39 · see citing works
Self-citationThe traveling heads: multicenter brain imaging at 7 Tesla
Voelker, M. N., Kraff, O., Brenner, D., Wollrab, A., Weinberger, O., Berger, M. C., Robinson, S., Bogner, W., Wiggins, C. J., Trampel, R., et al. (2016) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-016-0541-8 · Cited by 34 · 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
A Comparison of Five Methods for Signal Intensity Standardization in MRI
Bergeest, J.-P., & Jäger, F. (2008) · Informatik aktuell · doi:10.1007/978-3-540-78640-5_8 · Cited by 22 · see citing works
Otazo, R., Lin, F., Wiggins, G. C., Jordán, R., Sodickson, D. K., & Posse, S. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.049 · Cited by 21 · 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
Self-citationWhole Body MRI Intensity Standardization
Jäger, F., Nyúl, L. G., Frericks, B. B., Wacker, F. K., & Joachim Hornegger, A. (2007) · Springer eBooks · doi:10.1007/978-3-540-71091-2_92 · Cited by 15 · see citing works
放射線治療計画に対する代表的な4D-MRIを導出するためのダイナミックMRIの呼吸ベースソーティング | 文献情報 | J-GLOBAL 科学技術総合リンクセンター
Tryggestad, E. J., Flammang, A. J., Han-Oh, S., Russell, H., Joseph, H., Todd, M., Forbang, R. T., Steven, M. S., & John, W. (2013) · Medical Physics · Cited by 15 · see citing works
Local reconstruction of stenosed sections of artery using multiple MRA acquisitions
Herment, A., Roullot, E., Isabelle Bloch, A., Jolivet, O., Cesare, A. D., Frouin, F., Bittoun, J., & Mousseaux, É. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10435 · Cited by 14 · see citing works
A wavelet-based super-resolution method for multi-slice MRI
Islam, R., Lambert, A. J., Pickering, M. R., Scarvell, J. M., & Smith, P. N. (2012) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2012.512a109 · Cited by 12 · see citing works
Super-resolution reconstruction for tongue MR images
Woo, J., Bai, Y., Roy, S., Murano, E. Z., Stone, M., & Prince, J. L. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911445 · Cited by 12 · see citing works
Resolution enhancement of lung 4D‐CT via group‐sparsity
Bhavsar, A. V., Guorong Wu, A., Lian, J., & Shen, D. G. (2013) · Medical Physics · doi:10.1118/1.4829501 · Cited by 7 · see citing works
Self-citationSingle Image Super Resolution via Manifold ApproximationPreprint
Dang, C., & Radha, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.3349 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [9]Rabinson MD Chiu SJ Toth JA Izatt JA Lo JY Farsiu S New Applications of Super-Resolution in Medical Imaging, Book Chapter in Super-Resolution Imaging Farnham Taylor and Francis Group 2010 383 412
Cited by
Sorted by citations
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
MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection
Shi, J., Li, Z., Ying, S., Wang, C., Liu, Q., Zhang, Q., & Yan, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2843819 · Cited by 131 · see citing works
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping
Bollmann, S., Bøtker-Rasmussen, K. G., Kristensen, M., Blendal, R. G., Østergaard, L. R., Plocharski, M., O’Brien, K., Langkammer, C., Janke, A. L., & Barth, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.060 · Cited by 131 · see citing works
Super-resolution reconstruction of MR image with a novel residual learning network algorithm
Shi, J., Liu, Q., Wang, C., Zhang, Q., Ying, S., & Xu, H. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aab9e9 · Cited by 112 · see citing works
What’s new and what’s next in diffusion MRI preprocessing
Tax, C., Bastiani, M., Veraart, J., Garyfallidis, E., & Irfanoglu, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118830 · Cited by 107 · see citing works
Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains
Qu, L., Zhang, Y., Wang, S., Yap, P., & Shen, D. G. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101663 · Cited by 90 · see citing works
Self-citationPark, S., Lee, S. M., Do, K., Lee, J., Bae, W., Park, H., Jung, K.-H., & Seo, J. B. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0212 · Cited by 78 · see citing works
Progressive Sub-Band Residual-Learning Network for MR Image Super Resolution
Xue, X., Wang, Y., Li, J., Jiao, Z., Ren, Z., & Gao, X. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2945373 · Cited by 53 · see citing works
Long short‐term memory networks for proton dose calculation in highly heterogeneous tissues
Neishabouri, A., Wahl, N., Mairani, A., Köthe, U., & Bangert, M. (2020) · Medical Physics · doi:10.1002/mp.14658 · Cited by 48 · see citing works
Fan, M., Liu, Z., Xu, M., Wang, S., Zeng, T., Gao, X., & Li, L. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4345 · Cited by 45 · see citing works
Ultra-high field imaging in Major Depressive Disorder: a review of structural and functional studies
Cattarinussi, G., Delvecchio, G., Maggioni, E., Bressi, C., & Brambilla, P. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.04.056 · Cited by 39 · see citing works
Leveraging ultra-high field (7T) MRI in psychiatric research
Calabro, F. J., Parr, A. C., Sydnor, V. J., Hetherington, H. P., Prasad, K. M. R., Ibrahim, T. S., Sarpal, D. K., Famalette, A., Verma, P., & Luna, B. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01980-6 · Cited by 27 · see citing works
A multi-sequences MRI deep framework study applied to glioma classfication
Coupet, M., Urruty, T., Leelanupab, T., Naudin, M., Bourdon, P., Fernández-Maloigne, C., & Guillevin, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12316-1 · Cited by 27 · see citing works
Wavelet-based Semi-supervised Adversarial Learning for Synthesizing Realistic 7T from 3T MRI
Qu, L., Wang, S., Yap, P., & Shen, D. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_86 · Cited by 21 · see citing works
Self-citationRandom 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
Isallari, M., & Rekik, I. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102084 · Cited by 19 · see citing works
Self-citationMhiri, I., Khalifa, A. B., Mahjoub, M. A., & Rekik, I. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101768 · Cited by 18 · see citing works
Self-citationCurrent Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview
Martin, D., Tong, E., Kelly, B. S., Yeom, K. W., & Yedavalli, V. S. (2021) · Frontiers in Radiology · doi:10.3389/fradi.2021.713681 · Cited by 17 · see citing works
Duan, C., Bian, X., Cheng, K., Lyu, J., Xiong, Y., Xiao, S., Wang, X., Duan, Q., Li, C., Huang, J., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28944 · Cited by 16 · see citing works
Isallari, M., & Rekik, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_15 · Cited by 13 · see citing works
Self-citation7T MRI super-resolution with Generative Adversarial Network
Do, H., Bourdon, P., Helbert, D., Naudin, M., & Guillevin, R. (2021) · Electronic Imaging · doi:10.2352/issn.2470-1173.2021.18.3dia-106 · Cited by 13 · see citing works
Jiang, J., Qi, F., Du, H., Xu, J., Zhou, Y., Gao, D. Y., & Qiu, B. (2022) · IEEE Access · doi:10.1109/access.2022.3155226 · Cited by 12 · see citing works
Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine
Osman, A. F. I. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.84629 · Cited by 10 · see citing works
7T MRI Synthesization from 3T Acquisitions
Cui, Q., Tosun, D., Mukherjee, P., & Abbasi-Asl, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72104-5_4 · Cited by 9 · see citing works
Serrarens, C., Otter, M., Campforts, B. C. M., Schrander-Stumpel, C. T. R. M., Jansma, H., van Amelsvoort, T., & Vingerhoets, C. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09425-1 · Cited by 9 · see citing works
Ota, J., Umehara, K., Kershaw, J., Kishimoto, R., Hirano, Y., Tachibana, Y., Ohba, H., & Obata, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-14421-5 · Cited by 8 · see citing works
Douri, K., Iorio‐Morin, C., Mercure-Cyr, R., Figueiredo, G., Touchette, C. J., Masson‐Côté, L., & Mathieu, D. (2023) · Current Oncology · doi:10.3390/curroncol30110679 · Cited by 8 · see citing works
Super-Resolution of Brain MRI via U-Net Architecture
Kalluvila, A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140503 · Cited by 8 · see citing works
Yang, H., Liu, S., Liu, Y., Zhang, L., Huang, S., Zheng, J., Liu, J., Guo, H., Wu, E. X., & Lyu, M. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3597401 · Cited by 7 · see citing works
Hernandez, D., Han, Y., Son, S., & Kim, K. (2021) · Journal of Electromagnetic Waves and Applications · doi:10.1080/09205071.2021.1871779 · Cited by 7 · see citing works
Li, H., Prasad, R. G. N., Sekuboyina, A., Niu, C., Bai, S., Hemmert, W., & Menze, B. H. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434061 · Cited by 7 · see citing works
FS-RWKV: Leveraging Frequency Spatial-Aware RWKV for 3T-to-7T MRI Translation
Lei, Y., Li, Z., Liu, Y., Chen, X., & Pun, C. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356228 · Cited by 6 · see citing works
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
Sindel, A., Breininger, K., Kaesser, J., Heß, A., Maier, A., & Köhler, T. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451320 · Cited by 6 · see citing works
Liu, X., Qiu, S., Zhou, M., Le, W.-J., Li, Q., & Wang, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3536807 · Cited by 5 · see citing works
Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI
Yang, L., Wei, J., Li, Y., Wang, B., Guo, H., Yang, Y., & Xiang, J. (2021) · Brain Sciences · doi:10.3390/brainsci12010066 · Cited by 5 · see citing works
Carroll, M. C., Sommerville, R. S., Hay, K. E., Walker, A., Grigg, S., Kenny, L., Chua, B., Papacostas, J., Alexander, H., & Lin, C. Y. (2024) · Practical Radiation Oncology · doi:10.1016/j.prro.2024.09.007 · Cited by 4 · see citing works
Cramer, J. A., Ikuta, I., Baxter, L. C., Parker, J. J., Wang, Y., & Zhou, Y. (2025) · American Journal of Neuroradiology · doi:10.3174/ajnr.a9110 · Cited by 4 · see citing works
SQUID-Based Magnetic Resonance Imaging at Ultra-Low Field Using the Backprojection Method
Guo, Q., Ma, C., Zhang, X., Xu, Y., Fan, M., Yu, P., Hu, T., Chang, Y., & Yang, X. (2020) · Concepts in Magnetic Resonance Part B · doi:10.1155/2020/8882329 · Cited by 4 · see citing works
Diffusion-Based Graph Super-Resolution with Application to Connectomics
Rajadhyaksha, N., & Rekik, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46005-0_9 · Cited by 3 · see citing works
Self-citationIntracortical myelin across laminae in adult individuals with 47,XXX: a 7 Tesla MRI study
Serrarens, C., Ruiz-Fernandez, J., Otter, M., Campforts, B. C. M., Schrander-Stumpel, C. T. R. M., Linden, D. E. J., van Amelsvoort, T., Kashyap, S., & Vingerhoets, C. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae343 · Cited by 3 · see citing works
An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences
Coupet, M., Urruty, T., Leelanupab, T., Naudin, M., Bourdon, P., Fernández-Maloigne, C., & Guillevin, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63830-6_10 · Cited by 3 · see citing works
Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty
Li, H., Gopal, S., Sekuboyina, A., Zhang, J., Niu, C., Pirkl, C. M., Kirschke, J. S., Wiestler, B., & Menze, B. H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87735-4_5 · Cited by 3 · see citing works
Yao, R., Du, J., Peng, W., Li, H., Zhang, J., Lei, D., & Jia, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129847 · Cited by 2 · see citing works
Kaur, P., Sao, A. K., & Ahuja, C. K. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-021-02083-1 · Cited by 2 · see citing works
A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation
Lee, J., Oh, K., Shen, D. G., & Suk, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16443-9_32 · Cited by 2 · see citing works
Self-citationAcs, R., & Zhuang, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333499 · Cited by 1 · see citing works
Evolution of AI in Medical Imaging
Schaefferkoetter, J. D. (2022) · Springer eBooks · doi:10.1007/978-3-031-00119-2_4 · Cited by 1 · see citing works
Karthikeyan, P., & Rasmika, K. (2020) · The Journal of Laryngology & Otology · doi:10.1017/s0022215120001097
Dense Connected Super-Resolution Networks Based on Dual Learning
曾, 令. (2022) · Computer Science and Application · doi:10.12677/csa.2022.1212289
Serrarens, C., Otter, M., Campforts, B. C. M., Schrander-Stumpel, C. T. R. M., Jansma, H., van Amelsvoort, T., & Vingerhoets, C. (2022) · Figshare · doi:10.6084/m9.figshare.19226986
Yoon, D., Myong, Y., Kim, Y. G., Sim, Y., Cho, J.-H., Youngkyu, S., Cho, M., Oh, B., & Kim, S. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122042
Multi-modal feature transfer network for anisotropic 3D MRI image super-resolution
Li, H., Yao, R., Du, J., Zhu, H., Peng, W., & Jia, Y. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03204-3
FIDNet: Frequency-Informed Diffusion Network for Low-Field MRI Enhancement
Zhou, T., Liu, Q., Li, Z., Liang, P., Zhu, J., Fu, Y., Feng, Y., Liu, Q., Lu, R., Wei, H., et al. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3709378
- Authors
- Bahrami, K., Shi, F., Rekik, I., Gao, Y., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Shen, Dinggang
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2017-04-22
- Volume
- 44
- Issue
- 5
- Pages
- 1661-1677
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC5686784
- OpenAlex
- W2587341032
- Cited by
- 56
- References
- 56
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 4 · 2022: 10 · 2021: 10 · 2020: 8 · 2019: 6 · 2018: 5
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Physics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB008374)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- 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
- Keywords
- 7T MRI, MRI super-resolution, 3T MRI, sparse representation, brain tissue segmentation, white matter, gray matter, cerebrospinal fluid, high-resolution MRI
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Sensitivity and Specificity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
