Magnetic resonance fingerprinting

Compressed sensing

Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994

Compressed sensing

Donoho, D. L. (2004) · Cited by 16,703

Eigenfaces for Recognition

Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733

Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,715

Robust Face Recognition via Sparse Representation

Wright, J. N., Yang, A., Ganesh, A., Sastry, S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,398

A concordance correlation coefficient to evaluate reproducibility.

Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785

Sparse MRI: The application of compressed sensing for rapid MR imaging

Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952

Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

Candès, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,809

SENSE: Sensitivity encoding for fast MRI

Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5 · Cited by 6,303

Generalized autocalibrating partially parallel acquisitions (GRAPPA)

Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellús̆, V., Wang, J., Kiefer, B., & Haase, A. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10171 · Cited by 5,311

Dynamic T2 mapping by multi‐spin‐echo spatiotemporal encoding

Bao, Q., Ma, L., Liberman, G., Solomon, E., Martinho, R. P., & Frydman, L. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28158 · Cited by 12

qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks

Luu, H. M., Kim, D., Kim, J., Choi, S. H., & Park, S. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28411 · Cited by 12

Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications

Eck, B., Flamm, S. D., Kwon, D. H., Tang, W. H. W., Prieto, C., & Seiberlich, N. (2020) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2020.10.001 · Cited by 13

Whole brain 3D MR fingerprinting in multiple sclerosis: a pilot study

Mostardeiro, T. R., Panda, A., Campeau, N. G., Witte, R. J., Larson, N. B., Sui, Y., Lu, A., & McGee, K. P. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00620-5 · Cited by 12

T2 quantification in brain using 3D fast spin‐echo imaging with long echo trains

Snyder, J., McPhee, K. C., & Wilman, A. H. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29113 · Cited by 11

Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Rumac, S., Pavon, A. G., Hamilton, J., Rodrigues, D., Seiberlich, N., Schwitter, J., & van Heeswijk, R. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-23573-3 · Cited by 12

Internal Derangements of Joints—Past, Present, and Future

Sutter, R., Stoel, B. C., Buck, F. M., Andreisek, G., Morelli, J. N., Hodler, J., Li, X., & Pfirrmann, C. W. A. (2015) · Investigative Radiology · doi:10.1097/rli.0000000000000162 · Cited by 12

Dictionary-free MR Fingerprinting reconstruction of balanced-GRE sequencesPreprint

Sbrizzi, A., Bruijnen, T., van der Heide, O., Luijten, P. R., & van den Berg, C. A. T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08905 · Cited by 12

Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient

Grant‐Jacob, J. A., Everitt, C., Eason, R. W., King, L. J., & Mills, B. (2021) · Journal of Physics Communications · doi:10.1088/2399-6528/ac24d8 · Cited by 11

Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features

Fujita, S., Hagiwara, A., Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., Fukunaga, I., Kato, S., Akashi, T., Kamagata, K., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08555-3 · Cited by 11

Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study

Konar, A. S., Shah, A. D., Paudyal, R., Fung, M., Banerjee, S., Dave, A., Hatzoglou, V., & Shukla‐Dave, A. (2022) · Cancers · doi:10.3390/cancers14225606 · Cited by 11

An Exploration of a Phased-Array RF Coil for Very Low-Field Brain MRI

Shen, S., Zhang, Y., Kong, X., Yang, L., Rosen, M. S., & Xu, Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3341974 · Cited by 12

Unraveling contributions to the Z‐spectrum signal at 3.5 ppm of human brain tumors

Heo, H., Singh, M., Mahmud, S. Z., Blair, L., Kamson, D. O., & Zhou, J. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30241 · Cited by 11

Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification

Singh, M., Kang, B., Mahmud, S. Z., van Zijl, P. C. M., Zhou, J., & Heo, H. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30532 · Cited by 11

Multi-parameter molecular MRI quantification using physics-informed self-supervised learning

Finkelstein, A., Vladimirov, N., Zaiß, M., & Perlman, O. (2025) · Communications Physics · doi:10.1038/s42005-025-02063-8 · Cited by 11

Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning

Ding, Z., Morris, S., Hu, S., Su, T., Choi, J. Y., Blümcke, I., Wang, X. F., Sakaie, K., Murakami, H., Alexopoulos, A. V., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213691 · Cited by 11

Motion correction of multi-contrast images applied to T1 and T2 quantification in cardiac MRI

Menini, A., Slavin, G. S., Stainsby, J. A., Ferry, P., Felblinger, J., & Odille, F. (2014) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-014-0440-9 · Cited by 11

Medical image matching using variable randomized undersampling probability pattern in data acquisition

Jang, J., Eo, T., Kim, M., Choi, N., Dong-Yup, H., Kim, D., & Hwang, D. (2014) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/elinfocom.2014.6914453 · Cited by 11

Radiomic biomarkers informative of cancerous transformation in neurofibromatosis-1 plexiform tumors

Uthoff, J., Stefano, F. A. D., Panzer, K., Darbro, B. W., Sato, T. S., Khanna, R., Quelle, D. E., Meyerholz, D. K., Weimer, J. M., & Sieren, J. C. (2018) · Journal of Neuroradiology · doi:10.1016/j.neurad.2018.05.006 · Cited by 11

Quantitative Skeletal Muscle Imaging Using 3D MR Fingerprinting With Water and Fat Separation

Marty, B., Kolkovsky, A. L. L., Araújo, E. C. D. A., & Reyngoudt, H. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27381 · Cited by 11

UV-Induced fingerprint spectroscopy

Nejdl, L., Havlíková, M., Mravec, F., Vaculovič, T., Faltusová, V., Pavelicová, K., Baroň, M., Kumšta, M., Ondroušek, V., Adam, V., et al. (2021) · Food Chemistry · doi:10.1016/j.foodchem.2021.130499 · Cited by 11

T1 and T2 quantification using magnetic resonance fingerprinting in mild traumatic brain injury

Gerhalter, T., Cloos, M. A., Chen, A. M., Dehkharghani, S., Peralta, R., Babb, J. S., Zarate, A., Bushnik, T. L., Silver, J., Im, B., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08235-8 · Cited by 12

Repeatability of simultaneous 3D 1H MRF/23Na MRI in brain at 7 T

Rodriguez, G. G., Yu, Z., O’Donnell, L. F., Calderon, L. V., Cloos, M. A., & Madelin, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-18388-1 · Cited by 11

An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data

Slavkova, K. P., DiCarlo, J. C., Wadhwa, V., Kumar, S., Wu, C., Virostko, J. M., Yankeelov, T. E., & Tamir, J. I. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29547 · Cited by 11

3D magnetic resonance fingerprinting on a low-field 50 mT point-of-care system prototype: evaluation of muscle and lipid relaxation time mapping and comparison with standard techniques

O’Reilly, T., Börnert, P., Liu, H., WEBB, A. G., & Koolstra, K. (2023) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-023-01092-0 · Cited by 11

Joint MAPLE : Accelerated joint T1 and T2*$$ {{\mathrm{T}}_2}^{\ast } $$ mapping with scan‐specific self‐supervised networks

Heydari, A., Ahmadi, A., Kim, T. H., & Bilgiç, B. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29989 · Cited by 11

Two multinuclear GdIII macrocyclic complexes as contrast agents with high relaxivity and stability using rigid linkers

Zhao, G., Lu, C., Li, H., Xiao, Y., Zhang, W., Fang, X., Wang, P., Fang, X., Xu, J., & Yang, W. (2013) · Inorganica Chimica Acta · doi:10.1016/j.ica.2013.07.015 · Cited by 11

MRI postprocessing in presurgical evaluation

Wang, Z. I., & Alexopoulos, A. V. (2016) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000305 · Cited by 11

B1 inhomogeneity correction of RARE MRI with transceive surface radiofrequency probes

Delgado, P. R., Kuehne, A., Periquito, J. S., Millward, J. M., Pohlmann, A., Waiczies, S., & Niendorf, T. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28307 · Cited by 11

Emerging methods in radiology

Theek, B., Nolte, T., Pantke, D., Schrank, F., Gremse, F., Schulz, V., & Kiessling, F. M. (2020) · Der Radiologe · doi:10.1007/s00117-020-00696-0 · Cited by 12

Accelerated MR-STAT Reconstructions Using Sparse Hessian Approximations

van der Heide, O., Sbrizzi, A., & van den Berg, C. A. T. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3003893 · Cited by 11

Normal volumetric and T1 relaxation time values at 1.5 T in segmented pediatric brain MRI using a MP2RAGE acquisition

Morel, B., Piredda, G. F., Cottier, J., Tauber, C., Destrieux, C., Hilbert, T., Sirinelli, D., Thiran, J., Maréchal, B., & Kober, T. (2020) · European Radiology · doi:10.1007/s00330-020-07194-w · Cited by 11

A Comparison of T 2 Relaxation-Based MRI Stroke Timing Methods in Hyperacute Ischemic Stroke Patients: A Pilot Study

McGarry, B. L., Damion, R. A., Chew, I., Knight, M. J., Harston, G., Carone, D., Jezzard, P., Sitaram, A., Muir, K. W., Clatworthy, P. L., et al. (2020) · Journal of Central Nervous System Disease · doi:10.1177/1179573520943314 · Cited by 11

Lesion probability mapping in MS patients using a regression network on MR fingerprinting

Hermann, I., Golla, A., Martínez‐Heras, E., Schmidt, R., Solana, E., Llufriú, S., Gass, A., Schad, L. R., & Zöllner, F. G. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00636-x · Cited by 11

Magnetic resonance imaging of cystic fibrosis: Multi-organ imaging in the age of CFTR modulator therapies

McBennett, K., MacAskill, C. J., Keshock, E., Mahani, M. G., Mata, J. F., Towbin, A. J., Sankararaman, S., Drumm, M. L., Yu, X., Ren, C. L., et al. (2021) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2021.11.006 · Cited by 11

Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network

Liu, Y., Niu, H., Ren, P., Ren, J., Wei, X., Liu, W., Ding, H., Li, J., Xia, J., Zhang, T., et al. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac46dd · Cited by 13

Repeatability and robustness of MP‐GRASP T1 mapping

Li, Z., Xu, X., Yang, Y., & Feng, L. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29131 · Cited by 11

Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold Learning and Regularized Pre-Imaging

Zhou, Y., Wang, H., Liu, Y., Liang, D., & Ying, L. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3158904 · Cited by 11

Propagation of error from parameter constraints in quantitative MRI: Example application of multiple spin echo T2 mapping

Lankford, C. L., & Does, M. D. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26713 · Cited by 11

T 1 and T 2 quantification from standard turbo spin echo images

McPhee, K. C., & Wilman, A. H. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27495 · Cited by 11

Precision Brain Morphometry: Feasibility and Opportunities of Extreme Rapid ScansPreprint

Nielsen, J. A., Mair, R. W., Baker, J. T., & Buckner, R. L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/530436 · Cited by 11

Game of Learning Bloch Equation Simulations for MR FingerprintingPreprint

Yang, M., Jiang, Y., Ma, D., Mehta, B. B., & Griswold, M. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02270 · Cited by 11

MR fingerprinting of the prostate

Lo, W., Panda, A., Jiang, Y., Ahad, J., Gulani, V., & Seiberlich, N. (2022) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-022-01012-8 · Cited by 10

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

Sun, H., Wang, L., Daskivich, T. J., Qiu, S., Han, F., D’Agnolo, A., Saouaf, R., Christodoulou, A., Kim, H. L., Li, D., et al. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1223377 · Cited by 10

A Review of Online Adaptive Radiation Therapy

Lŭ, L., Zhang, Z., & Qi, P. (2024) · Applied Radiation Oncology · doi:10.37549/aro-d-24-00037 · Cited by 10

A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications

Çukur, T., Dar, S. U., Nezhad, V. A., Jun, Y., Kim, T. H., Fujita, S., & Bilgiç, B. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3617575 · Cited by 11

Material Fingerprinting: A shortcut to material model discovery without solving optimization problems

Flaschel, M., Martonová, D., Veil, C., & Ellen Kuhl, A. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.118573 · Cited by 10

Imaging the epileptic brain—time for new standards

Bernasconi, N., & Bernasconi, A. (2014) · Nature Reviews Neurology · doi:10.1038/nrneurol.2013.280 · Cited by 10

Dictionary-free MRI parameter estimation via kernel ridge regression

Nataraj, G., Nielsen, J., & Fessler, J. A. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950455 · Cited by 10

Magnetic resonance fingerprinting for preoperative differentiation between gonadotroph and non-gonadotroph pituitary macroadenomas

Bai, Y., Shen, Y. L., Chen, R., Zhang, X., Nittka, M., Koerzdoerfer, G., Gong, Q., & Wang, M. (2021) · European Radiology · doi:10.1007/s00330-021-07950-6 · Cited by 10

Quantitative T1 and T2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia

Stout, J. N., Liao, C., Gagoski, B. A., Türk, E. A., Feldman, H. A., Bibbo, C., Barth, W. H., Shainker, S. A., Wald, L. L., Grant, P. E., et al. (2021) · Placenta · doi:10.1016/j.placenta.2021.08.058 · Cited by 10

Relaxation time of brain tissue in the elderly assessed by synthetic MRI

Ndengera, M., Delattre, B. M. A., Scheffler, M., Lövblad, K., Meling, T. R., & Vargas, M. I. (2021) · Brain and Behavior · doi:10.1002/brb3.2449 · Cited by 10

Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain

Fujita, S., Hagiwara, A., Takei, N., Fukunaga, I., Hagiwara, Y., Ogawa, T., Hatano, T., Rettmann, D. W., Banerjee, S., Hwang, K., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119176 · Cited by 10

A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers

Fatania, K., Pirkl, C. M., Menzel, M. I., Hall, P. M., & Golbabaee, M. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761603 · Cited by 10

Direct synthesis of multi‐contrast brain MR images from MR multitasking spatial factors using deep learning

Qiu, S., Ma, S., Wang, L., Chen, Y., Fan, Z., Moser, F. G., Maya, M. M., Sati, P., Sicotte, N. L., Christodoulou, A., et al. (2023) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29715 · Cited by 10

Motion-compensated T1 mapping in cardiovascular magnetic resonance imaging: a technical review

Sheagren, C. D., Cao, T., Patel, J., Chen, Z., Lee, H., Wang, N., Christodoulou, A., & Wright, G. A. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1160183 · Cited by 10

Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool

Peretti, L., Donatelli, G., Cencini, M., Cecchi, P. C., Buonincontri, G., Cosottini, M., Tosetti, M., & Costagli, M. (2023) · Tomography · doi:10.3390/tomography9050137 · Cited by 10

Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compressed Sensing

Gómez, P. A., Ulaş, Ç., Sperl, J. I., Sprenger, T., Molina‐Romero, M., Menzel, M. I., & Menze, B. H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-28194-0_14 · Cited by 10

A 3D two-point method for whole-brain water content and relaxation time mapping: Comparison with gold standard methods

Schall, M., Zimmermann, M., Iordanishvili, E., Gu, Y., Shah, N. J., & Oros‐Peusquens, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201013 · Cited by 10

Geometry of Deep Learning for Magnetic Resonance Fingerprinting

Golbabaee, M., Chen, D., Gómez, P. A., Menzel, M. I., & Davies, M. E. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683549 · Cited by 10

A phantom system for assessing the effects of membrane lipids on water proton relaxation

Shtangel, O., & Mezer, A. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4209 · Cited by 10

Use of Multiplied, Added, Subtracted and/or FiTted Inversion Recovery (MASTIR) pulse sequences

Ma, Y., Fan, S., Shao, H., Du, J., Szeverenyi, N. M., YOUNG, I. R., & Bydder, G. M. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-568 · Cited by 10

Quantitative T1 and T1 Mapping

Boudreau, M., Keenan, K. E., & Stikov, N. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00004-4 · Cited by 10

Investigation of the effect of acquisition schemes on time-resolved magnetic resonance fingerprinting

Li, T., Cui, D., Ren, G., Hui, E. S. K., & Cai, J. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abf51f · Cited by 10

Analytical models of probe dynamics effects on NMR measurements

Greer, M., Ariando, D. J., Hürlimann, M. D., Song, Y., & Mandal, S. (2021) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2021.106975 · Cited by 10

Quantitative MR relaxation using MR fingerprinting with fractional-order signal evolution

Zou, L., Liang, D., Ye, H., Su, S. C., Zhu, Y., Liu, X., Zheng, H., & Wang, H. (2021) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2021.107042 · Cited by 10

Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC Within 3 Minutes

Liu, H., van der Heide, O., Mandija, S., van den Berg, C. A. T., & Sbrizzi, A. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3168436 · Cited by 10

Multicomponent MR fingerprinting reconstruction using joint‐sparsity and low‐rank constraints

Nagtegaal, M. A., Hartsema, E., Koolstra, K., & Vos, F. M. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29442 · Cited by 9

Development of a High-Throughput Ultrasound Technique for the Analysis of Tissue Engineering Constructs

Stukel, J. M., Goss, M., Zhou, H., Zhou, W., Willits, R. K., & Exner, A. A. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1507-0 · Cited by 10

Magnetic barcode imaging for contrast agents

Hung, A. H., Lilley, L. M., Hu, F., Harrison, V. S. R., & Meade, T. J. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26175 · Cited by 10

Parallel dynamic programming for optimal experiment design in nonlinear systems

Maidens, J., Packard, A., & Arcak, M. (2016) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2016.7798700 · Cited by 10

Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling

Zhao, B., Bilgiç, B., Elfar Adalsteinsson, A., Griswold, M. A., Wald, L. L., & Setsompop, K. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037553 · Cited by 10

A Fast GPU-optimized 3D MRI Simulator for Arbitrary k-space Sampling

Kose, R., Setoi, A., & Kose, K. (2018) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2018-0022 · Cited by 10

Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems

Bernstein, B., Liu, S., Papadaniil, C. D., & Fernandez‐Granda, C. (2020) · IEEE Transactions on Information Theory · doi:10.1109/tit.2020.2985015 · Cited by 10

Minimal Linear Networks for Magnetic Resonance Image Reconstruction

Liberman, G., & Poser, B. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-55763-x · Cited by 10

Correlation of T1- to T2-weighted signal intensity ratio with T1- and T2-relaxation time and IDH mutation status in glioma

Sanada, T., Yamamoto, S., Sakai, M., Umehara, T., Sato, H., Saito, M., Mitsui, N., Hiroshima, S., Anei, R., Kanemura, Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-23527-9 · Cited by 9

A fast MR fingerprinting simulator for direct error estimation and sequence optimization

Hu, S., Jordan, S. P., Boyacıoğlu, R., Rozada, I., Troyer, M., Griswold, M. A., McGivney, D., & Ma, D. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.01.011 · Cited by 9

Correcting synthetic MRI contrast-weighted images using deep learning

Kumar, S., Saber, H., Charron, O., Freeman, L. A., & Tamir, J. I. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.11.015 · Cited by 9

Enhancing MR vascular Fingerprinting with realistic microvascular geometries

Delphin, A., Boux, F., Brossard, C., Coudert, T., Warnking, J. M., Lemasson, B., Barbier, E., & Christen, T. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00377 · Cited by 10

Approximating R1 and R2 : A Quantitative Approach to Clinical Weighted MRI

Moskovich, S., Shtangel, O., & Mezer, A. (2024) · Human Brain Mapping · doi:10.1002/hbm.70102 · Cited by 9

Three‐dimensional high‐isotropic‐resolution MR fingerprinting optimized for 0. 55 T

Cao, X., Liao, C., Zhu, Z., Li, Z., Bhattacharjee, R., Nishmura, M., Wang, Z., Wang, N., Zhou, Z., Chen, Q., et al. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30420 · Cited by 9

Coordinate-based neural representation enabling zero-shot learning for fast 3D multiparametric quantitative MRI

Lao, G., Feng, R., Qi, H., Lv, Z., Liu, Q., Liu, C., Zhang, Y., & Wei, H. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103530 · Cited by 11

Vendor‐agnostic 3D multiparametric relaxometry improves cross‐platform reproducibility

Fujita, S., Gagoski, B. A., Nielsen, J., Zaitsev, M., Jun, Y., Cho, J., Yong, X., Uhl, Q., Xu, P., Milshteyn, E., et al. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30566 · Cited by 9

Low field, high impact: democratizing MRI for clinical and research innovation

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Stroke onset time determination using MRI relaxation times without non-ischaemic reference in a rat stroke model

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Three Dimensional Reconstruction Models for Medical Modalities: A Comprehensive Investigation and Analysis.

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Feasibility Study of MRI Muscles Molecular Imaging in Evaluation of Early Post-Mortem Interval

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Magnetic Resonance Imaging: Historical Overview, Technical Developments, and Clinical Applications

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Quantitative MRI to Characterize the Nucleus Pulposus Morphological and Biomechanical Variation According to Sagittal Bending Load and Radial Fissure, an ex vivo Ovine Specimen Proof-of-Concept Study

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State of the Art: Quantitative Cardiac MRI in Cardiac Amyloidosis

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High-Resolution 3D Magnetic Resonance Fingerprinting With a Graph Convolutional Network

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Ultrahigh field magnetic resonance imaging: new frontiers and possibilities in human imaging

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La risonanza magnetica fingerprinting: un nuovo approccio per la diagnostica medica

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STATEMENT Magnetic Resonance Fingerprinting- a promising new approach

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Magnetic resonance fingerprinting — Principles and first clinical results

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