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Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters.
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Hauptmann, A., Lucka, F., Betcke, M., Huynh, N., Adler, J., Cox, B., Beard, P., Ourselin, S., Arridge, S.: Model-based learning for accelerated, limited-view 3-D photoacoustic tomography. IEEE Trans. Med. Imag
2018
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Adler, J., Öktem, O.: Learned primal-dual reconstruction. IEEE Trans. Med. Imag
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Aggarwal, H.K., Mani, M.P., Jacob, M.: MoDL: Model-based deep learning architecture for inverse problems. IEEE Trans. Med. Imag
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Wen, J., Goyal, M.S., Astafiev, S.V., Raichle, M.E., Yablonskiy, D.A.: Genetically defined cellular correlates of the baseline brain MRI signal. PNAS
2018
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Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik
2018
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Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imag
2018
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Cai, C., Wang, C., Zeng, Y., Cai, S., Liang, D., Wu, Y., Chen, Z., Ding, X., Zhong, J.: Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network. Magn Reson Med
2018
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Yoon, J., Gong, E., Chatnuntawech, I., Bilgic, B., Lee, J., Jung, W., Ko, J., Jung, H., Setsompop, K., Zaharchuk, G., Kim, E.Y., Pauly, J., Lee, J.: Quantitative susceptibility mapping using deep neural network: QSMnet. NeuroImage
2018
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2020
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Liu, F.: Improving Quantitative Magnetic Resonance Imaging Using Deep Learning. Semin Musculoskelet Radiol
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Li, H., Yang, M., Kim, J., Liu, R., Zhang, C., Huang, P., Gaire, S.K., Liang, D., Li, X., Ying, L.: Ultra-fast simultaneous T1rho and T2 mapping using deep learning. In: ISMRM Annual Meeting (2020)
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Torop, M., Kothapalli, S.V.V.N., Sun, Y., Liu, J., Kahali, S., Yablonskiy, D.A., Kamilov, U.S.: Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact-free and denoised R2* images. Magn Reson Med
2020
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Jeelani, H., Yang, Y., Zhou, R., Kramer, C.M., Salerno, M., Weller, D.S.: A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1941–1944 (2020)
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Liu, F., Kijowski, R., Feng, L., El Fakhri, G.: High-performance rapid MR parameter mapping using model-based deep adversarial learning. Magnetic Resonance Imaging
2020
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Mukherjee, S., Carioni, M., Öktem, O., Schönlieb, C.-B.: End-to-end reconstruction meets data-driven regularization for inverse problems. In: Advances in Neural Information Processing Systems, vol. 34, pp. 21413–21425. Curran Associates, Inc., ??? (2021)
2021
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Kothapalli, S.V.V.N., Benzinger, T.L., Aschenbrenner, A.J., Perrin, R.J., Hildebolt, C.F., Goyal, M.S., Fagan, A.M., Raichle, M.E., Morris, J.C., Yablonskiy, D.A.: Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration That Precedes Tissue Atrophy in Early Alzheimer Disease (2021)
2021
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Roberts, N.T., Hinshaw, L.A., Colgan, T.J., Ii, T., Hernando, D., Reeder, S.B.: B0 and B1 inhomogeneities in the liver at 1.5 T and 3.0 T. Magn Reson Med
2021
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Gao, Y., Cloos, M., Liu, F., Crozier, S., Pike, G.B., Sun, H.: Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction. NeuroImage
2021
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Zeng, G., Guo, Y., Zhan, J., Wang, Z., Lai, Z., Du, X., Qu, X., Guo, D.: A review on deep learning MRI reconstruction without fully sampled k-space. BMC Medical Imaging
2021
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Sun, Y., Wu, Z., Xu, X., Wohlberg, B., Kamilov, U.S.: Scalable Plug-and-Play ADMM With Convergence Guarantees. IEEE Trans. Comput. Imaging
2021
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Kahali, S., Kothapalli, S.V.V.N., Xu, X., Kamilov, U.S., Yablonskiy, D.A.: Deep-learning-based accelerated and noise-suppressed estimation (DANSE) of quantitative gradient recalled echo (qGRE) MRI metrics associated with human brain neuronal structure and hemodynamic properties. bioRxiv (2021)
2021
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Liu, F., Kijowski, R., El Fakhri, G., Feng, L.: Magnetic resonance parameter mapping using model-guided self-supervised deep learning. Magn Reson Med
2021
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2022
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Jung, W., Bollmann, S., Lee, J.: Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR Biomed
2022
Closest in time.
Feng, L., Ma, D., Liu, F.: Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR Biomed
2022
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2022
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2024
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Zimmermann, F.F., Kolbitsch, C., Schuenke, P., Kofler, A.: Pinqi: an end-to-end physics-informed approach to learned quantitative mri reconstruction. IEEE Transactions on Computational Imaging (2024)
2024
Closest in time.