21.11.1.1 Magnetic Resonance Imaging, Image Reconstruction

Ramos-Llordén, G., den Dekker, A.J., Sijbers, J.,
Partial Discreteness: A Novel Prior for Magnetic Resonance Image Reconstruction,
MedImg(36), No. 5, May 2017, pp. 1041-1053.
IEEE DOI 1609
Bayes methods, Gaussian mixture model, Image reconstruction, Magnetic resonance imaging, Probability density function, Reconstruction algorithms, Gaussian Mixture Model, MRI reconstruction, partial discreteness, segmentation, sparsity BibRef

Jia, Y., Gholipour, A., He, Z., Warfield, S.K.,
A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans,
MedImg(36), No. 5, May 2017, pp. 1182-1193.
IEEE DOI 1705
Magnetic resonance imaging (MRI), orthogonal scans, overcomplete dictionary, partial volume effects (PVEs), sparse representation, super-resolution (SR), wavelet, fusion BibRef

Saucedo, A., Lefkimmiatis, S., Rangwala, N., Sung, K.,
Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments,
MedImg(36), No. 6, June 2017, pp. 1209-1220.
IEEE DOI 1706
Acceleration, Image reconstruction, Magnetic resonance imaging, Optimization, Partitioning algorithms, Transforms, Compressive sensing, locally low-rank regularization, parallel imaging, parameter, mapping BibRef

Liu, S.J.[Shu-Jun], Cao, J.X.[Jian-Xin], Liu, H.Q.[Hong-Qing], Shen, X.D.[Xiao-Dong], Zhang, K.[Kui], Wang, P.[Pin],
MRI reconstruction using a joint constraint in patch-based total variational framework,
JVCIR(46), No. 1, 2017, pp. 150-164.
Elsevier DOI 1706
CS-MRI BibRef

Sakhaee, E., Entezari, A.,
Joint Inverse Problems for Signal Reconstruction via Dictionary Splitting,
SPLetters(24), No. 8, August 2017, pp. 1203-1207.
IEEE DOI 1708
discrete wavelet transforms, inverse problems, signal reconstruction, signal representation, Bregman splitting, DWT, SWT, dictionary splitting approach, discrete wavelet transform, joint inverse problems, orthogonal dictionaries, parallel sparse recovery problems, signal reconstruction, sparse signal recovery, Image reconstruction, Noise reduction, Redundancy, shift-invariant wavelet transform, sparse, reconstruction BibRef

Selvi, G.U.V.[G. Uma Vetri], Nadarajan, R.,
CT and MRI image compression using wavelet-based contourlet transform and binary array technique,
RealTimeIP(13), No. 2, June 2017, pp. 261-272.
WWW Link. 1708
BibRef

Chen, Z., Fu, Y., Xiang, Y., Rong, R.,
A Novel Iterative Shrinkage Algorithm for CS-MRI via Adaptive Regularization,
SPLetters(24), No. 10, October 2017, pp. 1443-1447.
IEEE DOI 1710
Newton method, biomedical MRI, compressed sensing, BibRef

Panic, M., Aelterman, J., Crnojevic, V., Pižurica, A.,
Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior,
MedImg(36), No. 10, October 2017, pp. 2104-2115.
IEEE DOI 1710
Markov processes, biomedical MRI, compressed sensing, image reconstruction, medical BibRef

Wübbeler, G.[Gerd], Elster, C.[Clemens],
A Large-Scale Optimization Method Using a Sparse Approximation of the Hessian for Magnetic Resonance Fingerprinting,
SIIMS(10), No. 3, 2017, pp. 979-1004.
DOI Link 1710
BibRef

Ma, T.H.[Tian-Hui], Lou, Y.F.[Yi-Fei], Huang, T.Z.[Ting-Zhu],
Truncated L_1-2 Models for Sparse Recovery and Rank Minimization,
SIIMS(10), No. 3, 2017, pp. 1346-1380.
DOI Link 1710
BibRef

Wang, S., Tan, S., Gao, Y., Liu, Q., Ying, L., Xiao, T., Liu, Y., Liu, X., Zheng, H., Liang, D.,
Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging,
MedImg(37), No. 1, January 2018, pp. 251-261.
IEEE DOI 1801
biomedical MRI, compressed sensing, image coding, image denoising, image reconstruction, image representation, joint sparsity BibRef

Palacios, B.[Benjamin], Uhlmann, G.[Gunther], Wang, Y.[Yiran],
Reducing Streaking Artifacts in Quantitative Susceptibility Mapping,
SIIMS(10), No. 4, 2017, pp. 1921-1934.
DOI Link 1801

See also Inverse Problem in Quantitative Susceptibility Mapping. BibRef

Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.,
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction,
MedImg(37), No. 2, February 2018, pp. 491-503.
IEEE DOI 1802
Compressed sensing, Image reconstruction, Imaging, Machine learning, Neural networks, Redundancy, image reconstruction BibRef

Speidel, T.[Tobias], Paul, J.[Jan], Wundrak, S.[Stefan], Rasche, V.[Volker],
Quasi-Random Single-Point Imaging Using Low-Discrepancy k-Space Sampling,
MedImg(37), No. 2, February 2018, pp. 473-479.
IEEE DOI 1802
Animals, Compressed sensing, Image reconstruction, Imaging, Metals, Trajectory, Single-point imaging, BibRef

Abascal, J.F.P.J., Desco, M., Parra-Robles, J.,
Incorporation of Prior Knowledge of Signal Behavior Into the Reconstruction to Accelerate the Acquisition of Diffusion MRI Data,
MedImg(37), No. 2, February 2018, pp. 547-556.
IEEE DOI 1802
Acceleration, Gases, Image reconstruction, Lungs, TV, Ventilation, Compressed sensing, hyperpolarized gas MRI, lung diffusion MRI, split Bregman method BibRef

Levine, E., Hargreaves, B.,
On-the-Fly Adaptive k-Space Sampling for Linear MRI Reconstruction Using Moment-Based Spectral Analysis,
MedImg(37), No. 2, February 2018, pp. 557-567.
IEEE DOI 1802
Acceleration, Eigenvalues and eigenfunctions, Image reconstruction, Sensitivity, k-space sampling, parallel MRI BibRef

Baxter, J.S.H., Hosseini, Z., Peters, T.M., Drangova, M.,
Cyclic Continuous Max-Flow: A Third Paradigm in Generating Local Phase Shift Maps in MRI,
MedImg(37), No. 2, February 2018, pp. 568-579.
IEEE DOI 1802
Imaging, Linear programming, Manifolds, Mathematical model, Optimization, Robustness, Topology, MRI phase processing, variational optimization BibRef

Rabanillo, I., Aja-Fernández, S., Alberola-López, C., Hernando, D.,
Exact Calculation of Noise Maps and g-Factor in GRAPPA Using a k-Space Analysis,
MedImg(37), No. 2, February 2018, pp. 480-490.
IEEE DOI 1802
Convolution, Covariance matrices, Estimation, Image reconstruction, Kernel, GRAPPA, Magnetic resonance imaging, g-factor, parallel imaging BibRef

Deka, B., Datta, S., Handique, S.,
Wavelet Tree Support Detection for Compressed Sensing MRI Reconstruction,
SPLetters(25), No. 5, May 2018, pp. 730-734.
IEEE DOI 1805
Computational complexity, Data models, Hidden Markov models, Image reconstruction, Magnetic resonance imaging, Training, wavelet tree support BibRef

Bao, S., Wang, P., Mok, T.C.W., Chung, A.C.S.,
3D Randomized Connection Network With Graph-Based Label Inference,
IP(27), No. 8, August 2018, pp. 3883-3892.
IEEE DOI 1806
biomedical MRI, feedforward neural nets, graph theory, image segmentation, inference mechanisms, magnetic resonance imaging BibRef

Yang, G.[Guang], Yu, S.[Simiao], Dong, H.[Hao], Slabaugh, G.[Greg], Dragotti, P.L.[Pier Luigi], Ye, X.J.[Xu-Jiong], Liu, F.D.[Fang-De], Arridge, S.[Simon], Keegan, J.[Jennifer], Guo, Y.K.[Yi-Ke], Firmin, D.[David],
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction,
MedImg(37), No. 6, June 2018, pp. 1310-1321.
IEEE DOI 1806
Acceleration, Encoding, Image reconstruction, Machine learning, Transforms, Compressed sensing, de-aliasing, deep learning, fast MRI, magnetic resonance imaging (MRI) BibRef

Gözcü, B., Mahabadi, R.K., Li, Y.H., Ilicak, E., Çukur, T., Scarlett, J., Cevher, V.,
Learning-Based Compressive MRI,
MedImg(37), No. 6, June 2018, pp. 1394-1406.
IEEE DOI 1806
Compressed sensing, Decoding, Image reconstruction, Machine learning, Magnetic resonance imaging, learning-based subsampling BibRef

Quan, T.M., Nguyen-Duc, T., Jeong, W.K.,
Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss,
MedImg(37), No. 6, June 2018, pp. 1488-1497.
IEEE DOI 1806
Databases, Image quality, Image reconstruction, Machine learning, Magnetic resonance imaging, Training, MRI BibRef

Bustin, A., Voilliot, D., Menini, A., Felblinger, J., de Chillou, C., Burschka, D., Bonnemains, L., Odille, F.,
Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning,
MedImg(37), No. 8, August 2018, pp. 1932-1942.
IEEE DOI 1808
Image reconstruction, Magnetic resonance imaging, Image resolution, Dictionaries, super-resolution BibRef

Datta, S.[Sumit], Deka, B.[Bhabesh],
Efficient interpolated compressed sensing reconstruction scheme for 3D MRI,
IET-IPR(12), No. 11, November 2018, pp. 2119-2127.
DOI Link 1810
BibRef

Vogt, T.[Thomas], Lellmann, J.[Jan],
Measure-Valued Variational Models with Applications to Diffusion-Weighted Imaging,
JMIV(60), No. 9, November 2018, pp. 1482-1502.
Springer DOI 1810
BibRef

Yang, Y.Y.[Yun-Yun], Qin, X.[Xuxu], Wu, B.Y.[Bo-Ying],
Fast and accurate compressed sensing model in magnetic resonance imaging with median filter and split Bregman method,
IET-IPR(13), No. 1, January 2019, pp. 1-8.
DOI Link 1812
BibRef

Lyu, J., Nakarmi, U., Liang, D., Sheng, J., Ying, L.,
KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction,
MedImg(38), No. 1, January 2019, pp. 312-321.
IEEE DOI 1901
Coils, Calibration, Image reconstruction, Sensitivity, Kernel, Magnetic resonance imaging, Kernel, nonlinear model, parallel imaging BibRef

Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V., Rueckert, D.,
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,
MedImg(38), No. 1, January 2019, pp. 280-290.
IEEE DOI 1901
Image reconstruction, Magnetic resonance imaging, Optimization, Machine learning, Iterative methods, Recurrent neural networks, cardiac image reconstruction BibRef

Mardani, M., Gong, E., Cheng, J.Y., Vasanawala, S.S., Zaharchuk, G., Xing, L., Pauly, J.M.,
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI,
MedImg(38), No. 1, January 2019, pp. 167-179.
IEEE DOI 1901
Image reconstruction, Manifolds, Magnetic resonance imaging, Training, Generators, Neural networks, compressed sensing (CS) BibRef

Zhao, B., Haldar, J.P., Liao, C., Ma, D., Jiang, Y., Griswold, M.A., Setsompop, K., Wald, L.L.,
Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics,
MedImg(38), No. 3, March 2019, pp. 844-861.
IEEE DOI 1903
Magnetization, Mathematical model, Signal to noise ratio, Biomedical imaging, Image reconstruction, quantitative imaging BibRef

Kretzler, M., Hamilton, J., Griswold, M., Seiberlich, N.,
a-f BLAST: Non-Iterative Radial k-t BLAST Reconstruction for Real-Time Imaging,
MedImg(38), No. 3, March 2019, pp. 775-790.
IEEE DOI 1903
Image reconstruction, Discrete Fourier transforms, Dynamics, Magnetic resonance imaging, spatio-temporal domain BibRef

Karsa, A., Shmueli, K.,
SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase,
MedImg(38), No. 6, June 2019, pp. 1347-1357.
IEEE DOI 1906
Magnetic resonance imaging, Merging, Standards, Brain, Phase distortion, susceptibility mapping BibRef

Tezcan, K.C., Baumgartner, C.F., Luechinger, R., Pruessmann, K.P., Konukoglu, E.,
MR Image Reconstruction Using Deep Density Priors,
MedImg(38), No. 7, July 2019, pp. 1633-1642.
IEEE DOI 1907
Image reconstruction, Reconstruction algorithms, Training, Data models, Signal processing algorithms, Mathematical model, density estimation BibRef

Dong, G., Hintermüller, M., Papafitsoros, K.,
Quantitative Magnetic Resonance Imaging: From Fingerprinting to Integrated Physics-Based Models,
SIIMS(12), No. 2, 2019, pp. 927-971.
DOI Link 1907
BibRef

Shahdloo, M., Ilicak, E., Tofighi, M., Saritas, E.U., Çetin, A.E., Çukur, T.,
Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI,
MedImg(38), No. 7, July 2019, pp. 1677-1689.
IEEE DOI 1907
Image reconstruction, Coils, TV, Calibration, Magnetic resonance imaging, Compressed sensing (CS), multi-acquisition BibRef

Huynh, K.M., Chen, G., Wu, Y., Shen, D., Yap, P.,
Multi-Site Harmonization of Diffusion MRI Data via Method of Moments,
MedImg(38), No. 7, July 2019, pp. 1599-1609.
IEEE DOI 1907
Magnetic resonance imaging, Method of moments, Sociology, Statistics, Protocols, Anisotropic magnetoresistance, method of moments BibRef

Hu, Y., Liu, X., Jacob, M.,
A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery,
MedImg(38), No. 8, August 2019, pp. 1841-1851.
IEEE DOI 1908
Convolution, Image reconstruction, Magnetic resonance imaging, Jacobian matrices, Optimization, TV, Approximation algorithms, image recovery BibRef

Fang, Z., Chen, Y., Liu, M., Xiang, L., Zhang, Q., Wang, Q., Lin, W., Shen, D.,
Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting,
MedImg(38), No. 10, October 2019, pp. 2364-2374.
IEEE DOI 1910
BibRef
And: Erratum: MedImg(39), No. 2, February 2020, pp. 543-543.
IEEE DOI 2002
Imaging, Feature extraction, Dictionaries, Deep learning, Mathematical model, Acceleration, Learning systems, tissue quantification BibRef

Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.,
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks,
MedImg(38), No. 10, October 2019, pp. 2375-2388.
IEEE DOI 1910
Magnetic resonance imaging, Image generation, Generative adversarial networks, cycleconsistency loss BibRef

Stolk, C.C., Sbrizzi, A.,
Understanding the Combined Effect of k-Space Undersampling and Transient States Excitation in MR Fingerprinting Reconstructions,
MedImg(38), No. 10, October 2019, pp. 2445-2455.
IEEE DOI 1910
Image reconstruction, Mathematical model, Transient analysis, Radio frequency, Magnetization, Perturbation methods, quantitative MRI BibRef

Zhu, Q.Y.[Qing-Yong], Ren, Y.[Yanan], Qiu, Z.L.[Zhi-Lang], Wang, W.[Wei],
Robust MR image super-resolution reconstruction with cross-modal edge-preserving regularization,
IJIST(29), No. 4, 2019, pp. 491-500.
DOI Link 1911
alternating optimization, cross modal, edge preserving, MRI super-resolution, multimodal registration BibRef

Jiang, M.F.[Ming-Feng], Lu, L.[Liang], Shen, Y.[Yi], Wu, L.[Long], Gong, Y.L.[Ying-Lan], Xia, L.[Ling], Liu, F.[Feng],
Directional tensor product complex tight framelets for compressed sensing MRI reconstruction,
IET-IPR(13), No. 12, October 2019, pp. 2183-2189.
DOI Link 1911
BibRef

Zeng, W.[Wei], Peng, J.[Jie], Wang, S.S.[Shan-Shan], Liu, Q.[Qiegen],
A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond,
SP:IC(81), 2020, pp. 115701.
Elsevier DOI 1912
MRI reconstruction, Super-resolution, Cascade network, Dense connection BibRef

Doneva, M.,
Mathematical Models for Magnetic Resonance Imaging Reconstruction: An Overview of the Approaches, Problems, and Future Research Areas,
SPMag(37), No. 1, January 2020, pp. 24-32.
IEEE DOI 2001
Image reconstruction, Magnetic resonance imaging, Magnetic fields, Magnetic field measurement, Compressed sensing, Encoding BibRef

Fessler, J.A.,
Optimization Methods for Magnetic Resonance Image Reconstruction: Key Models and Optimization Algorithms,
SPMag(37), No. 1, January 2020, pp. 33-40.
IEEE DOI 2001
Magnetic resonance imaging, Image reconstruction, Cost function, Signal processing algorithms, Compressed sensing, Computational modeling BibRef

Wen, B., Ravishankar, S., Pfister, L., Bresler, Y.,
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-Based Learning to Building Neural Networks,
SPMag(37), No. 1, January 2020, pp. 41-53.
IEEE DOI 2001
Magnetic resonance imaging, Image reconstruction, Transforms, Computational modeling, Adaptation models, Compressed sensing BibRef

Jacob, M., Mani, M.P., Ye, J.C.,
Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning,
SPMag(37), No. 1, January 2020, pp. 54-68.
IEEE DOI 2001
Magnetic resonance imaging, Signal processing algorithms, Compressed sensing, Extrapolation, Sensitivity, Interpolation BibRef

Aggarwal, H.K., Mani, M.P., Jacob, M.,
MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI,
MedImg(39), No. 4, April 2020, pp. 1268-1277.
IEEE DOI 2004
Magnetic resonance imaging, Convolution, Deep learning, Image reconstruction, Distortion, Computational complexity, convolutional neural network BibRef

Haldar, J.P., Setsompop, K.,
Linear Predictability in Magnetic Resonance Imaging Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging,
SPMag(37), No. 1, January 2020, pp. 69-82.
IEEE DOI 2001
Magnetic resonance imaging, Image reconstruction, Calibration, Compressed sensing, Extrapolation, Data models BibRef

Tamir, J.I., Ong, F., Anand, S., Karasan, E., Wang, K., Lustig, M.,
Computational MRI With Physics-Based Constraints: Application to Multicontrast and Quantitative Imaging,
SPMag(37), No. 1, January 2020, pp. 94-104.
IEEE DOI 2001
Magnetic resonance imaging, Magnetization, Mathematical model, Radio frequency, Image reconstruction, Computational modeling, Compressed sensing BibRef

Ahmad, R., Bouman, C.A., Buzzard, G.T., Chan, S., Liu, S., Reehorst, E.T., Schniter, P.,
Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery,
SPMag(37), No. 1, January 2020, pp. 105-116.
IEEE DOI 2001
Magnetic resonance imaging, Signal processing algorithms, Compressed sensing, Noise reduction, Optimization, Acceleration BibRef

Knoll, F., Hammernik, K., Zhang, C., Moeller, S., Pock, T., Sodickson, D.K., Akcakaya, M.,
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues,
SPMag(37), No. 1, January 2020, pp. 128-140.
IEEE DOI 2001
Image reconstruction, Magnetic resonance imaging, Compressed sensing, Machine learning BibRef

Liang, D., Cheng, J., Ke, Z., Ying, L.,
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks,
SPMag(37), No. 1, January 2020, pp. 141-151.
IEEE DOI 2001
Image reconstruction, Magnetic resonance imaging, Compressed sensing, Deep learning, Optimization, Reconstruction algorithms BibRef

Han, Y., Sunwoo, L., Ye, J.C.,
k-Space Deep Learning for Accelerated MRI,
MedImg(39), No. 2, February 2020, pp. 377-386.
IEEE DOI 2002
Deep learning, Convolution, Neural networks, Matrix converters, Matrix decomposition, Interpolation, Signal representation, convolution framelets BibRef

Song, P.F.[Ping-Fan], Weizman, L.[Lior], Mota, J.F.C.[João F. C.], Eldar, Y.C.[Yonina C.], Rodrigues, M.R.D.[Miguel R. D.],
Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction,
MedImg(39), No. 3, March 2020, pp. 621-633.
IEEE DOI 2004
BibRef
Earlier: ICIP18(2880-2884)
IEEE DOI 1809
Image reconstruction, Magnetic resonance imaging, Machine learning, Dictionaries, Noise reduction, Correlation, MR fingerprinting. Matching pursuit algorithms, guidance information BibRef

Park, S., Chen, L., Townsend, J., Lee, H., Feinberg, D.A.,
Simultaneous Multi-VENC and Simultaneous Multi-Slice Phase Contrast Magnetic Resonance Imaging,
MedImg(39), No. 3, March 2020, pp. 742-752.
IEEE DOI 2004
Radio frequency, Magnetic resonance imaging, Encoding, Steady-state, Image reconstruction, CAIPIRINHA BibRef

Song, G., Sun, Y., Liu, J., Wang, Z., Kamilov, U.S.,
A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network,
SPLetters(27), 2020, pp. 451-455.
IEEE DOI 2004
Image reconstruction, Magnetic resonance imaging, Head, Recurrent neural networks, Task analysis, deep learning BibRef

Sharma, A., Hamarneh, G.,
Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network,
MedImg(39), No. 4, April 2020, pp. 1170-1183.
IEEE DOI 2004
Magnetic resonance imaging, Pipelines, Tumors, Decoding, Generative adversarial networks, multi-modal, synthesis BibRef

Jeyaraj, P.R.[Pandia Rajan], Nadar, E.R.S.[Edward Rajan Samuel],
High-performance dynamic magnetic resonance image reconstruction and synthesis employing deep feature learning convolutional networks,
IJIST(30), No. 2, 2020, pp. 380-390.
DOI Link 2005
dynamic MRI image reconstruction, fine-tuned deep learning, MR image synthesis, visualization BibRef

Ong, F., Uecker, M., Lustig, M.,
Accelerating Non-Cartesian MRI Reconstruction Convergence Using k-Space Preconditioning,
MedImg(39), No. 5, May 2020, pp. 1646-1654.
IEEE DOI 2005
Image reconstruction, Convergence, Magnetic resonance imaging, Gradient methods, Acceleration, Trajectory, MRI, density compensation BibRef

Zhang, J.C.[Jia-Cheng], Brindise, M.C.[Melissa C.], Rothenberger, S.[Sean], Schnell, S.[Susanne], Markl, M.[Michael], Saloner, D.[David], Rayz, V.L.[Vitaliy L.], Vlachos, P.P.[Pavlos P.],
4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares,
MedImg(39), No. 5, May 2020, pp. 1668-1680.
IEEE DOI 2005
Magnetic resonance imaging, Velocity measurement, Pressure measurement, Biomedical measurement, weighted least-squares BibRef

El Hajj, C.[Christian], Moussaoui, S.[Saïd], Collewet, G.[Guylaine], Musse, M.[Maja],
Multi-Exponential Transverse Relaxation Times Estimation From Magnetic Resonance Images Under Rician Noise and Spatial Regularization,
IP(29), 2020, pp. 6721-6733.
IEEE DOI 2007
BibRef
Earlier:
Spatially Regularized Multi-Exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise,
ICIP19(1143-1147)
IEEE DOI 1910
Magnetic resonance imaging, Rician channels, Minimization, Parameter estimation, Signal to noise ratio, T2~relaxation times. Maximum-Likelihood, Spatial Regularization, Majoration-Minimization, Rician noise, Multi-T2 BibRef

Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.,
Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis,
MedImg(39), No. 7, July 2020, pp. 2339-2350.
IEEE DOI 2007
Image synthesis, Training, Adaptation models, Biomedical imaging, Feature extraction, brain BibRef

Lazarus, C.[Carole], März, M.[Maximilian], Weiss, P.[Pierre],
Correcting the Side Effects of ADC Filtering in MR Image Reconstruction,
JMIV(62), No. 6-7, July 2020, pp. 1034-1047.
Springer DOI 2007
BibRef

Wang, G., Gong, E., Banerjee, S., Martin, D., Tong, E., Choi, J., Chen, H., Wintermark, M., Pauly, J.M., Zaharchuk, G.,
Synthesize High-Quality Multi-Contrast Magnetic Resonance Imaging From Multi-Echo Acquisition Using Multi-Task Deep Generative Model,
MedImg(39), No. 10, October 2020, pp. 3089-3099.
IEEE DOI 2010
Magnetic resonance imaging, Generators, Machine learning, Generative adversarial networks, Training, Image reconstruction, image fusion BibRef

Cai, J.F.[Jian-Feng], Choi, J.K.[Jae Kyu], Wei, K.[Ke],
Data Driven Tight Frame for Compressed Sensing MRI Reconstruction via Off-the-Grid Regularization,
SIIMS(13), No. 3, 2020, pp. 1272-1301.
DOI Link 2010
BibRef

Liu, R., Zhang, Y., Cheng, S., Luo, Z., Fan, X.,
A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling,
MedImg(39), No. 12, December 2020, pp. 4150-4163.
IEEE DOI 2012
Image reconstruction, Optimization, Reliability, Rician channels, Magnetic resonance imaging, Machine learning, Compressed sensing, Rician noise BibRef

Cao, C.H.[Chun-Hong], Duan, W.[Wei], Hu, K.[Kai], Xiao, F.[Fen],
Compressive sensing MR imaging based on adaptive tight frame and reference image,
IET-IPR(14), No. 14, December 2020, pp. 3508-3515.
DOI Link 2012
BibRef

Edupuganti, V., Mardani, M., Vasanawala, S., Pauly, J.,
Uncertainty Quantification in Deep MRI Reconstruction,
MedImg(40), No. 1, January 2021, pp. 239-250.
IEEE DOI 2012
Image reconstruction, Uncertainty, Magnetic resonance imaging, Biomedical imaging, Data models, Training, Probabilistic logic, SURE BibRef

Lei, K., Mardani, M., Pauly, J.M., Vasanawala, S.S.,
Wasserstein GANs for MR Imaging: From Paired to Unpaired Training,
MedImg(40), No. 1, January 2021, pp. 105-115.
IEEE DOI 2012
Training, Image reconstruction, Magnetic resonance imaging, Task analysis, Generators, Wasserstein training BibRef

Shen, M.[Marui], Li, J.C.[Jin-Cheng], Zhang, T.[Tao], Zou, J.[Jian],
Magnetic resonance imaging reconstruction via non-convex total variation regularization,
IJIST(31), No. 1, 2021, pp. 412-424.
DOI Link 2102
ADMM, minmax-concave penalty, Moreau envelope, MRI reconstruction, TV regularization BibRef

Zhao, C., Dewey, B.E., Pham, D.L., Calabresi, P.A., Reich, D.S., Prince, J.L.,
SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning,
MedImg(40), No. 3, March 2021, pp. 805-817.
IEEE DOI 2103
Magnetic resonance imaging, Training data, Protocols, anti-aliasing BibRef

Qi, H.[Haikun], Cruz, G.[Gastao], Botnar, R.[René], Prieto, C.[Claudia],
Synergistic multi-contrast cardiac magnetic resonance image reconstruction,
Royal(A: 379), No. 2200, June 2021, pp. 20200197.
DOI Link 2107
BibRef

Wang, X.Q.[Xiao-Qing], Tan, Z.G.[Zheng-Guo], Scholand, N.[Nick], Roeloffs, V.[Volkert], Uecker, M.[Martin],
Physics-based reconstruction methods for magnetic resonance imaging,
Royal(A: 379), No. 2200, June 2021, pp. 20200196.
DOI Link 2107
BibRef

Lv, J.[Jun], Zhu, J.[Jin], Yang, G.[Guang],
Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction,
Royal(A: 379), No. 2200, June 2021, pp. 20200203.
DOI Link 2107
BibRef

You, D.[Di], Zhang, J.[Jian], Xie, J.F.[Jing-Fen], Chen, B.[Bin], Ma, S.W.[Si-Wei],
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing,
IP(30), 2021, pp. 6066-6080.
IEEE DOI 2107
Image reconstruction, Training, Compressed sensing, Task analysis, Magnetic resonance imaging, Inverse problems, optimization-inspired BibRef

Gutierrez, A.[Alex], Mullen, M.[Michael], Xiao, D.[Di], Jang, A.[Albert], Froelich, T.[Taylor], Garwood, M.[Michael], Haupt, J.[Jarvis],
Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification,
MedImg(40), No. 9, September 2021, pp. 2477-2486.
IEEE DOI 2109
Image reconstruction, Magnetic resonance imaging, Computational modeling, Mathematical model, Encoding, sparsification BibRef

Xuan, K.[Kai], Si, L.P.[Li-Ping], Zhang, L.[Lichi], Xue, Z.[Zhong], Jiao, Y.N.[Yi-Ning], Yao, W.W.[Wei-Wu], Shen, D.G.[Ding-Gang], Wu, D.J.[Di-Jia], Wang, Q.[Qian],
Reducing magnetic resonance image spacing by learning without ground-truth,
PR(120), 2021, pp. 108103.
Elsevier DOI 2109
Generative adversarial network, Magnetic resonance imaging, Super-resolution, Variational auto-encoder BibRef

Muckley, M.J.[Matthew J.], Riemenschneider, B.[Bruno], Radmanesh, A.[Alireza], Kim, S.[Sunwoo], Jeong, G.[Geunu], Ko, J.Y.[Jing-Yu], Jun, Y.[Yohan], Shin, H.[Hyungseob], Hwang, D.[Dosik], Mostapha, M.[Mahmoud], Arberet, S.[Simon], Nickel, D.[Dominik], Ramzi, Z.[Zaccharie], Ciuciu, P.[Philippe], Starck, J.L.[Jean-Luc], Teuwen, J.[Jonas], Karkalousos, D.[Dimitrios], Zhang, C.[Chaoping], Sriram, A.[Anuroop], Huang, Z.[Zhengnan], Yakubova, N.[Nafissa], Lui, Y.W.[Yvonne W.], Knoll, F.[Florian],
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction,
MedImg(40), No. 9, September 2021, pp. 2306-2317.
IEEE DOI 2109
Magnetic resonance imaging, Image reconstruction, Acceleration, Machine learning, Data models, Training, Pathology, Challenge, optimization BibRef

Guo, P.F.[Peng-Fei], Wang, P.[Puyang], Yasarla, R.[Rajeev], Zhou, J.Y.[Jin-Yuan], Patel, V.M.[Vishal M.], Jiang, S.S.[Shan-Shan],
Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs,
MedImg(40), No. 10, October 2021, pp. 2832-2844.
IEEE DOI 2110
Lesions, Image synthesis, Training, Magnetic resonance imaging, Image segmentation, Generative adversarial networks, Decoding, segmentation BibRef

Guruprasad, S.[Shrividya], Bharathi, S.H., Anto Ramesh Delvi, D.,
Effective compressed sensing MRI reconstruction via hybrid GSGWO algorithm,
JVCIR(80), 2021, pp. 103274.
Elsevier DOI 2110
Compressed sensing, Cross guided bilateral filter, Quasi random sampling, Hybridized Walsh Hadamard Transform and Discrete Wavelet Transform BibRef

Zou, Q.[Qing], Ahmed, A.H.[Abdul Haseeb], Nagpal, P.[Prashant], Kruger, S.[Stanley], Jacob, M.[Mathews],
Dynamic Imaging Using a Deep Generative SToRM (Gen-SToRM) Model,
MedImg(40), No. 11, November 2021, pp. 3102-3112.
IEEE DOI 2111
Manifolds, Storms, Magnetic resonance imaging, Time series analysis, Generators, Imaging, Electronics packaging, deep image prior BibRef

Lahiri, A.[Anish], Wang, G.H.[Guan-Hua], Ravishankar, S.[Saiprasad], Fessler, J.A.[Jeffrey A.],
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction,
MedImg(40), No. 11, November 2021, pp. 3113-3124.
IEEE DOI 2111
Image reconstruction, Dictionaries, Supervised learning, Transforms, Training data, Training, Reconstruction algorithms, sparse representations BibRef

Oh, G.[Gyutaek], Lee, J.E.[Jeong Eun], Ye, J.C.[Jong Chul],
Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation,
MedImg(40), No. 11, November 2021, pp. 3125-3139.
IEEE DOI 2111
Motion artifacts, Magnetic resonance imaging, Deep learning, Image reconstruction, Imaging, Transient analysis, Training, MRI BibRef

Yarnykh, V.L.[Vasily L.],
Data-Driven Retrospective Correction of B1 Field Inhomogeneity in Fast Macromolecular Proton Fraction and R1 Mapping,
MedImg(40), No. 12, December 2021, pp. 3473-3484.
IEEE DOI 2112
Protons, Radio frequency, Magnetization, Magnetic resonance imaging, Nonhomogeneous media, T1 relaxation BibRef

Quan, C.[Cong], Zhou, J.J.[Jin-Jie], Zhu, Y.Z.[Yuan-Zheng], Chen, Y.[Yang], Wang, S.S.[Shan-Shan], Liang, D.[Dong], Liu, Q.[Qiegen],
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction,
MedImg(40), No. 12, December 2021, pp. 3265-3278.
IEEE DOI 2112
Image reconstruction, Magnetic resonance imaging, Training, Standards, Encoding, Data models, Unsupervised learning, homotopic gradients BibRef

Dhengre, N.[Nikhil], Sinha, S.[Saugata],
An edge guided cascaded U-net approach for accelerated magnetic resonance imaging reconstruction,
IJIST(31), No. 4, 2021, pp. 2014-2022.
DOI Link 2112
compressive sensing magnetic resonance imaging reconstruction, U-net BibRef

Yang, X.M.[Xiao-Mei], Mei, Y.[Yubo], Hu, X.Y.[Xun-Yong], Luo, R.S.[Rui-Seng], Liu, K.[Kai],
Compressed Sensing MRI by Integrating Deep Denoiser and Weighted Schatten P-Norm Minimization,
SPLetters(29), 2022, pp. 21-25.
IEEE DOI 2202
Magnetic resonance imaging, Image reconstruction, Noise reduction, Indexes, AWGN, TV, Noise level, Deep prior, FFDNet, Plug-and-play BibRef

Chen, Y.T.[Yu-Tong], Schönlieb, C.B.[Carola-Bibiane], Liò, P.[Pietro], Leiner, T.[Tim], Dragotti, P.L.[Pier Luigi], Wang, G.[Ge], Rueckert, D.[Daniel], Firmin, D.[David], Yang, G.[Guang],
AI-Based Reconstruction for Fast MRI: A Systematic Review and Meta-Analysis,
PIEEE(110), No. 2, February 2022, pp. 224-245.
IEEE DOI 2202
Deep learning, Systematics, Magnetic resonance imaging, Neural networks, Complexity theory, Artificial intelligence, neural network BibRef

Narnhofer, D.[Dominik], Effland, A.[Alexander], Kobler, E.[Erich], Hammernik, K.[Kerstin], Knoll, F.[Florian], Pock, T.[Thomas],
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction,
MedImg(41), No. 2, February 2022, pp. 279-291.
IEEE DOI 2202
Magnetic resonance imaging, Image reconstruction, Uncertainty, Bayes methods, Optimal control, Inverse problems, Estimation, Bayes' theorem BibRef

Guo, L.[Li], Lyu, J.[Jian], Zhang, Z.[Zhe], Shi, J.P.[Jin-Ping], Feng, Q.J.[Qian-Jin], Feng, Y.Q.[Yan-Qiu], Gao, M.Y.[Ming-Yong], Zhang, X.Y.[Xin-Yuan],
A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information,
MedImg(41), No. 2, February 2022, pp. 308-319.
IEEE DOI 2202
Tensors, Estimation, Fitting, Noise reduction, Medical diagnostic imaging, Signal to noise ratio, prior information BibRef

Mehta, R.[Raghav], Christinck, T.[Thomas], Nair, T.[Tanya], Bussy, A.[Aurélie], Premasiri, S.[Swapna], Costantino, M.[Manuela], Chakravarthy, M.M.[M. Mallar], Arnold, D.L.[Douglas L.], Gal, Y.[Yarin], Arbel, T.[Tal],
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference,
MedImg(41), No. 2, February 2022, pp. 360-373.
IEEE DOI 2202
Task analysis, Uncertainty, Lesions, Biomedical imaging, Image segmentation, Deep learning, Magnetic resonance imaging, classification BibRef

Park, J.[Juhyung], Jung, W.[Woojin], Choi, E.J.[Eun-Jung], Oh, S.H.[Se-Hong], Jang, J.H.[Jin-Hee], Shin, D.[Dongmyung], An, H.J.[Hong-Jun], Lee, J.[Jongho],
DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value,
MedImg(41), No. 2, February 2022, pp. 491-499.
IEEE DOI 2202
Diffusion tensor imaging, Image reconstruction, Deep learning, Training, Protons, Training data, Deep learning, dMRI reconstruction, magnetic resonance imaging BibRef

Wang, H.Q.[Hao-Qian], Hu, X.W.[Xiao-Wan], Zhao, X.L.[Xiao-Le], Zhang, Y.L.[Yu-Lun],
Wide Weighted Attention Multi-Scale Network for Accurate MR Image Super-Resolution,
CirSysVideo(32), No. 3, March 2022, pp. 962-975.
IEEE DOI 2203
Feature extraction, Convolution, Superresolution, Task analysis, Image reconstruction, Medical diagnostic imaging, Deep learning, weighted fusion BibRef

Hu, Y.[Yue], Li, P.[Peng], Chen, H.[Hao], Zou, L.X.[Li-Xian], Wang, H.F.[Hai-Feng],
High-Quality MR Fingerprinting Reconstruction Using Structured Low-Rank Matrix Completion and Subspace Projection,
MedImg(41), No. 5, May 2022, pp. 1150-1164.
IEEE DOI 2205
Image reconstruction, Dictionaries, Magnetic resonance imaging, Data models, Pattern matching, Heuristic algorithms, Sensitivity, subspace projection BibRef

Qiao, Y.C.[Yu-Chuan], Shi, Y.G.[Yong-Gang],
Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI,
MedImg(41), No. 5, May 2022, pp. 1165-1175.
IEEE DOI 2205
Distortion, Nonlinear distortion, Image coding, Magnetic resonance imaging, Imaging, fiber orientation distribution BibRef

Inda, A.J.G.[Adan Jafet Garcia], Huang, S.Y.[Shao Ying], Imamoglu, N.[Nevrez], Yu, W.W.[Wen-Wei],
Physics-Coupled Neural Network Magnetic Resonance Electrical Property Tomography (MREPT) for Conductivity Reconstruction,
IP(31), 2022, pp. 3463-3478.
IEEE DOI 2205
Image reconstruction, Physics, Magnetic resonance imaging, Artificial neural networks, Conductivity, Training, physics coupled BibRef

Sui, Y.[Yao], Afacan, O.[Onur], Jaimes, C.[Camilo], Gholipour, A.[Ali], Warfield, S.K.[Simon K.],
Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction,
MedImg(41), No. 6, June 2022, pp. 1383-1399.
IEEE DOI 2206
Spatial resolution, Magnetic resonance imaging, Signal to noise ratio, Training, Imaging, Image reconstruction, patient-specific learning BibRef

Eda, N.[Naohiro], Fushimi, M.[Motofumi], Hasegawa, K.[Keisuke], Nara, T.[Takaaki],
A Method for Electrical Property Tomography Based on a Three-Dimensional Integral Representation of the Electric Field,
MedImg(41), No. 6, June 2022, pp. 1400-1409.
IEEE DOI 2206
Magnetic resonance imaging, Laplace equations, Image reconstruction, Magnetic domains, Iterative methods, Helmholtz decomposition BibRef

Korkmaz, Y.[Yilmaz], Dar, S.U.H.[Salman U. H.], Yurt, M.[Mahmut], Özbey, M.[Muzaffer], Çukur, T.[Tolga],
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers,
MedImg(41), No. 7, July 2022, pp. 1747-1763.
IEEE DOI 2207
Magnetic resonance imaging, Image reconstruction, Transformers, Data models, Adaptation models, Training, Task analysis, Adversarial, generative BibRef

Cengiz, S.[Sevim], Yildirim, M.[Muhammed], Bas, A.[Abdullah], Ozturk-Isik, E.[Esin],
ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis,
IJIST(32), No. 4, 2022, pp. 1068-1083.
DOI Link 2207
BibRef
And: Correction: IJIST(33), No. 2, 2023, pp. 770-770.
DOI Link 2303
magnetic resonance spectroscopic imaging, MR spectral data analysis, open-source, software BibRef

Ramzi, Z.[Zaccharie], Chaithya, G.R., Starck, J.L.[Jean-Luc], Ciuciu, P.[Philippe],
NC-PDNet: A Density-Compensated Unrolled Network for 2D and 3D Non-Cartesian MRI Reconstruction,
MedImg(41), No. 7, July 2022, pp. 1625-1638.
IEEE DOI 2207
Image reconstruction, Magnetic resonance imaging, Neural networks, Optimization, Sensitivity, Iterative methods, MRI, unrolled networks BibRef

Tezcan, K.C.[Kerem C.], Karani, N.[Neerav], Baumgartner, C.F.[Christian F.], Konukoglu, E.[Ender],
Sampling Possible Reconstructions of Undersampled Acquisitions in MR Imaging With a Deep Learned Prior,
MedImg(41), No. 7, July 2022, pp. 1885-1896.
IEEE DOI 2207
Uncertainty, Image reconstruction, Noise measurement, Measurement uncertainty, Training, Task analysis, unsupervised learning BibRef

Chaithya, G.R., Weiss, P.[Pierre], Daval-Frérot, G.[Guillaume], Massire, A.[Aurélien], Vignaud, A.[Alexandre], Ciuciu, P.[Philippe],
Optimizing Full 3D SPARKLING Trajectories for High-Resolution Magnetic Resonance Imaging,
MedImg(41), No. 8, August 2022, pp. 2105-2117.
IEEE DOI 2208
Trajectory, Hardware, Optimization, Magnetic resonance imaging, Spirals, Signal to noise ratio, 3D MRI, optimization, non-Cartesian, acceleration BibRef

Chen, E.Z.[Eric Z.], Wang, P.[Puyang], Chen, X.[Xiao], Chen, T.[Terrence], Sun, S.[Shanhui],
Pyramid Convolutional RNN for MRI Image Reconstruction,
MedImg(41), No. 8, August 2022, pp. 2033-2047.
IEEE DOI 2208
Image reconstruction, Magnetic resonance imaging, Optimization, Data models, High frequency, Brain modeling, Training, multi-scale learning BibRef

Fan, C.C.[Chen-Chen], Peng, L.[Liang], Wang, T.[Tian], Yang, H.J.[Hong-Jun], Zhou, X.H.[Xiao-Hu], Ni, Z.L.[Zhen-Liang], Wang, G.[Guan'an], Chen, S.[Sheng], Zhou, Y.J.[Yan-Jie], Hou, Z.G.[Zeng-Guang],
TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network,
MedImg(41), No. 8, August 2022, pp. 1925-1937.
IEEE DOI 2208
Magnetic resonance imaging, Generative adversarial networks, Task analysis, Training, Generators, Data models, generative adversarial network BibRef

Islam, R.[Rafiqul], Islam, M.S.[Md Shafiqul], Uddin, M.S.[Muhammad Shahin],
Compressed Sensing in Parallel MRI: A Review,
IJIG(22), No. 4, July 2022, pp. 2250038.
DOI Link 2208
BibRef

Zhang, X.L.[Xin-Lin], Lu, H.F.[Heng-Fa], Guo, D.[Di], Lai, Z.Y.[Zong-Ying], Ye, H.H.[Hui-Hui], Peng, X.[Xi], Zhao, B.[Bo], Qu, X.B.[Xiao-Bo],
Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization,
MedImg(41), No. 9, September 2022, pp. 2486-2498.
IEEE DOI 2209
Image reconstruction, Imaging, Magnetic resonance imaging, Computational modeling, Correlation, Memory management, low-rank Hankel matrix BibRef

Xuan, K.[Kai], Xiang, L.[Lei], Huang, X.Q.[Xiao-Qian], Zhang, L.[Lichi], Liao, S.[Shu], Shen, D.G.[Ding-Gang], Wang, Q.[Qian],
Multimodal MRI Reconstruction Assisted With Spatial Alignment Network,
MedImg(41), No. 9, September 2022, pp. 2499-2509.
IEEE DOI 2209
Magnetic resonance imaging, Image reconstruction, Redundancy, Image registration, Deep learning, Task analysis, Image synthesis, multi-modal registration BibRef

Wang, G.H.[Guan-Hua], Luo, T.R.[Tian-Rui], Nielsen, J.F.[Jon-Fredrik], Noll, D.C.[Douglas C.], Fessler, J.A.[Jeffrey A.],
B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI,
MedImg(41), No. 9, September 2022, pp. 2318-2330.
IEEE DOI 2209
Trajectory, Image reconstruction, Magnetic resonance imaging, Splines (mathematics), Training, Reconstruction algorithms, Kernel, image reconstruction BibRef

Bian, C.C.[Cong Chao], Cao, N.[Ning], Mao, M.H.[Ming He],
CSDL-Net: An iterative network based on compressed sensing and deep learning,
IJIST(32), No. 5, 2022, pp. 1511-1520.
DOI Link 2209
compressed sensing, deep neural network, image reconstruction, magnetic resonance imaging BibRef

Liu, H.Y.[Hong-Yan], van der Heide, O.[Oscar], Mandija, S.[Stefano], van den Berg, C.A.T.[Cornelis A. T.], Sbrizzi, A.[Alessandro],
Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC Within 3 Minutes,
MedImg(41), No. 10, October 2022, pp. 2681-2692.
IEEE DOI 2210
Image reconstruction, Mathematical models, Computational modeling, Magnetization, Radio frequency, neural network BibRef

Joseph, J.[Jiffy], Hemanth, C.[Challa], Narayanan, P.P.[Pournami Pulinthanathu], Balakrishnan, J.P.[Jayaraj Pottekkattuvalappil], Puzhakkal, N.[Niyas],
Computed tomography image generation from magnetic resonance imaging using Wasserstein metric for MR-only radiation therapy,
IJIST(32), No. 6, 2022, pp. 2080-2093.
DOI Link 2212
computed tomography, generative adversarial network, magnetic resonance imaging, Wasserstein GAN, Wasserstein metric BibRef

Hasse, A.[Adam], Bertini, J.[Julian], Foxley, S.[Sean], Jeong, Y.[Yong], Javed, A.[Adil], Carroll, T.J.[Timothy J.],
Application of a novel T1 retrospective quantification using internal references (T1-REQUIRE) algorithm to derive quantitative T1 relaxation maps of the brain,
IJIST(32), No. 6, 2022, pp. 1903-1915.
DOI Link 2212
MRI, quantification, T1 relaxometry, T1-weighted, validation BibRef

Yurt, M.[Mahmut], Dalmaz, O.[Onat], Dar, S.[Salman], Ozbey, M.[Muzaffer], Tinaz, B.[Berk], Oguz, K.[Kader], Çukur, T.[Tolga],
Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths,
MedImg(41), No. 12, December 2022, pp. 3895-3906.
IEEE DOI 2212
Magnetic resonance imaging, Training, Computational modeling, Image reconstruction, Generative adversarial networks, undersampled BibRef

Xie, Z.H.[Zhong-Hua], Liu, L.J.[Ling-Jun],
Transferring Deep Gaussian Denoiser for Compressed Sensing MRI Reconstruction,
MultMedMag(29), No. 4, October 2022, pp. 5-13.
IEEE DOI 2301
Image reconstruction, Noise reduction, Training, Magnetic resonance imaging, Optimization, Gradient methods, transfer learning BibRef

Kontogiannis, A.[Alexandros], Juniper, M.P.[Matthew P.],
Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem,
IP(32), 2023, pp. 281-294.
IEEE DOI 2301
Image reconstruction, Compressed sensing, Boundary conditions, Noise measurement, Magnetic resonance imaging, velocity reconstruction and segmentation BibRef

Wang, Z.[Zi], Qian, C.[Chen], Guo, D.[Di], Sun, H.W.[Hong-Wei], Li, R.[Rushuai], Zhao, B.[Bo], Qu, X.B.[Xiao-Bo],
One-Dimensional Deep Low-Rank and Sparse Network for Accelerated MRI,
MedImg(42), No. 1, January 2023, pp. 79-90.
IEEE DOI 2301
Image reconstruction, Training, Magnetic resonance imaging, Iron, Convolution, Imaging, Deep learning, Deep learning, fast imaging, sparse BibRef

Hammernik, K.[Kerstin], Küstner, T.[Thomas], Yaman, B.[Burhaneddin], Huang, Z.N.[Zheng-Nan], Rueckert, D.[Daniel], Knoll, F.[Florian], Akçakaya, M.[Mehmet],
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging,
SPMag(40), No. 1, January 2023, pp. 98-114.
IEEE DOI 2301
Deep learning, Inverse problems, Magnetic resonance imaging, Computational modeling, Pipelines, Signal processing, Task analysis BibRef

Kamilov, U.S.[Ulugbek S.], Bouman, C.A.[Charles A.], Buzzard, G.T.[Gregery T.], Wohlberg, B.[Brendt],
Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications,
SPMag(40), No. 1, January 2023, pp. 85-97.
IEEE DOI 2301
Training data, Machine learning algorithms, Computational modeling, Magnetic resonance imaging, Signal processing algorithms BibRef

Qu, W.Y.[Wen-Yi], Cheng, J.[Jing], Zhu, Y.J.[Yan-Jie], Liang, D.[Dong],
Deep MR parametric imaging with the learned L+S model and attention mechanism,
IET-IPR(17), No. 4, 2023, pp. 969-978.
DOI Link 2303
biomedical imaging, image reconstruction, medical image processing BibRef

Yang, Q.Q.[Qin-Qin], Wang, Z.[Zi], Guo, K.Y.[Kun-Yuan], Cai, C.B.[Cong-Bo], Qu, X.B.[Xiao-Bo],
Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The imaging physics-based data synthesis paradigm for artificial intelligence,
SPMag(40), No. 2, March 2023, pp. 129-140.
IEEE DOI 2303
Deep learning, Privacy, Biological system modeling, Image processing, Magnetic resonance, Tablet computers, Synthetic data BibRef

Abergel, R.[Rémy], Boussâa, M.[Mehdi], Durand, S.[Sylvain], Frapart, Y.M.[Yves-Michel],
Electron Paramagnetic Resonance Image Reconstruction with Total Variation Regularization,
IPOL(13), 2023, pp. 90-139.
DOI Link 2303
BibRef

Aggarwal, H.K.[Hemant Kumar], Pramanik, A.[Aniket], John, M.[Maneesh], Jacob, M.[Mathews],
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms,
MedImg(42), No. 4, April 2023, pp. 1133-1144.
IEEE DOI 2304
Measurement, Loss measurement, Training, Image reconstruction, Noise measurement, Magnetic resonance imaging, MRI BibRef

Yang, J.W.[Jun-Wei], Li, X.X.[Xiao-Xin], Liu, F.[Feihong], Nie, D.[Dong], Lio, P.[Pietro], Qi, H.[Haikun], Shen, D.G.[Ding-Gang],
Fast Multi-Contrast MRI Acquisition by Optimal Sampling of Information Complementary to Pre-Acquired MRI Contrast,
MedImg(42), No. 5, May 2023, pp. 1363-1373.
IEEE DOI 2305
Magnetic resonance imaging, Image reconstruction, Optimization, Gold, Deep learning, Task analysis, Neural networks, Deep learning, under-sampling pattern BibRef

Gao, Z.F.[Zhi-Fan], Guo, Y.F.[Yi-Feng], Zhang, J.J.[Jia-Jing], Zeng, T.Y.[Tie-Yong], Yang, G.[Guang],
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI,
MedImg(42), No. 6, June 2023, pp. 1859-1874.
IEEE DOI 2306
Magnetic resonance imaging, Image reconstruction, Feature extraction, Visual perception, Image restoration, generative adversarial networks BibRef

Yi, Z.Y.[Zhe-Yuan], Hu, J.H.[Jia-Hao], Zhao, Y.[Yujiao], Xiao, L.F.[Lin-Fang], Liu, Y.L.[Yi-Long], Leong, A.T.L.[Alex T. L.], Chen, F.[Fei], Wu, E.X.[Ed X.],
Fast and Calibrationless Low-Rank Parallel Imaging Reconstruction Through Unrolled Deep Learning Estimation of Multi-Channel Spatial Support Maps,
MedImg(42), No. 6, June 2023, pp. 1644-1655.
IEEE DOI 2306
Image reconstruction, Imaging, Deep learning, Sensitivity, Calibration, Convolution, Estimation, complex-valued network BibRef

Song, Y.Z.[Yi-Zhuang], Sadleir, R.[Rosalind], Liu, J.[Jijun],
Convergence Analysis of the Harmonic B_(z) Algorithm with Single Injection Current in MREIT,
SIIMS(16), No. 2, 2023, pp. 706-739.
DOI Link 2306
Magnetic resonance electrical impedance tomography. BibRef

Elmas, G.[Gokberk], Dar, S.U.H.[Salman U. H.], Korkmaz, Y.[Yilmaz], Ceyani, E.[Emir], Susam, B.[Burak], Ozbey, M.[Muzaffer], Avestimehr, S.[Salman], Çukur, T.[Tolga],
Federated Learning of Generative Image Priors for MRI Reconstruction,
MedImg(42), No. 7, July 2023, pp. 1996-2009.
IEEE DOI 2307
Magnetic resonance imaging, Image reconstruction, Adaptation models, Data models, Training, collaborative BibRef

Feng, C.M.[Chun-Mei], Yan, Y.L.[Yun-Lu], Wang, S.S.[Shan-Shan], Xu, Y.[Yong], Shao, L.[Ling], Fu, H.Z.[Hua-Zhu],
Specificity-Preserving Federated Learning for MR Image Reconstruction,
MedImg(42), No. 7, July 2023, pp. 2010-2021.
IEEE DOI 2307
Image reconstruction, Training, Servers, Privacy, Data privacy, Head, Collaborative work, MR image reconstruction, federated learning BibRef

Liu, Y.Y.[Yuan-Yuan], Liang, D.[Dong], Cui, Z.X.[Zhuo-Xu], Yang, Y.X.[Yu-Xin], Cao, C.[Chentao], Zhu, Q.Y.[Qing-Yong], Cheng, J.[Jing], Shi, C.[Caiyun], Wang, H.F.[Hai-Feng], Zhu, Y.J.[Yan-Jie],
Accelerating Magnetic Resonance T_1rho Mapping Using Simultaneously Spatial Patch-Based and Parametric Group-Based Low-Rank Tensors (SMART),
MedImg(42), No. 8, August 2023, pp. 2247-2261.
IEEE DOI 2308
Tensors, Image reconstruction, Biomedical imaging, Magnetic resonance imaging, Magnetic resonance, Correlation, patch BibRef

Feng, C.M.[Chun-Mei], Yan, Y.L.[Yun-Lu], Chen, G.[Geng], Xu, Y.[Yong], Hu, Y.[Ying], Shao, L.[Ling], Fu, H.Z.[Hua-Zhu],
Multimodal Transformer for Accelerated MR Imaging,
MedImg(42), No. 10, October 2023, pp. 2804-2816.
IEEE DOI 2310
BibRef

Li, Y.H.[Yong-Hao], Zhou, T.[Tao], He, K.[Kelei], Zhou, Y.[Yi], Shen, D.G.[Ding-Gang],
Multi-Scale Transformer Network With Edge-Aware Pre-Training for Cross-Modality MR Image Synthesis,
MedImg(42), No. 11, November 2023, pp. 3395-3407.
IEEE DOI Code:
WWW Link. 2311
BibRef

Peng, H.[Hong], Jiang, C.[Chen], Cheng, J.[Jing], Zhang, M.H.[Ming-Hui], Wang, S.S.[Shan-Shan], Liang, D.[Dong], Liu, Q.[Qiegen],
One-Shot Generative Prior in Hankel-k-Space for Parallel Imaging Reconstruction,
MedImg(42), No. 11, November 2023, pp. 3420-3435.
IEEE DOI 2311
BibRef

Cui, Z.X.[Zhuo-Xu], Jia, S.[Sen], Cheng, J.[Jing], Zhu, Q.Y.[Qing-Yong], Liu, Y.Y.[Yuan-Yuan], Zhao, K.[Kankan], Ke, Z.[Ziwen], Huang, W.Q.[Wen-Qi], Wang, H.F.[Hai-Feng], Zhu, Y.J.[Yan-Jie], Ying, L.[Leslie], Liang, D.[Dong],
Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging,
MedImg(42), No. 12, December 2023, pp. 3540-3554.
IEEE DOI 2312
BibRef

Alaifari, R.[Rima], Alberti, G.S.[Giovanni S.], Gauksson, T.[Tandri],
Short Communication: Localized Adversarial Artifacts for Compressed Sensing MRI,
SIIMS(16), No. 4, 2023, pp. SC14-SC26.
DOI Link 2312
BibRef

Yang, Y.[Yan], Wang, Y.Z.[Yi-Zhou], Wang, J.Z.[Jia-Zhen], Sun, J.[Jian], Xu, Z.B.[Zong-Ben],
An Unrolled Implicit Regularization Network for Joint Image and Sensitivity Estimation in Parallel MR Imaging with Convergence Guarantee,
SIIMS(16), No. 3, 2023, pp. 1791-1824.
DOI Link 2312
BibRef

Zach, M.[Martin], Knoll, F.[Florian], Pock, T.[Thomas],
Stable Deep MRI Reconstruction Using Generative Priors,
MedImg(42), No. 12, December 2023, pp. 3817-3832.
IEEE DOI 2312
BibRef

Sun, K.C.[Kai-Cong], Wang, Q.[Qian], Shen, D.G.[Ding-Gang],
Joint Cross-Attention Network With Deep Modality Prior for Fast MRI Reconstruction,
MedImg(43), No. 1, January 2024, pp. 558-569.
IEEE DOI Code:
WWW Link. 2401
BibRef

Wang, Q.[Qi], Wen, Z.J.[Zhi-Jie], Shi, J.[Jun], Wang, Q.[Qian], Shen, D.G.[Ding-Gang], Ying, S.H.[Shi-Hui],
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction,
MedImg(43), No. 11, November 2024, pp. 3924-3935.
IEEE DOI 2411
Image reconstruction, Magnetic resonance imaging, Task analysis, Manifolds, Deep learning, Transformers, Reliability, optimal transport BibRef

Guo, P.F.[Peng-Fei], Mei, Y.Q.[Yi-Qun], Zhou, J.Y.[Jin-Yuan], Jiang, S.S.[Shan-Shan], Patel, V.M.[Vishal M.],
ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer,
MedImg(43), No. 1, January 2024, pp. 582-593.
IEEE DOI Code:
WWW Link. 2401
BibRef

Li, X.[Xing], Yang, Y.[Yan], Zheng, H.R.[Hai-Rong], Xu, Z.B.[Zong-Ben],
ISP-IRLNet: Joint optimization of interpretable sampler and implicit regularization learning network for accerlerated MRI,
PR(151), 2024, pp. 110412.
Elsevier DOI 2404
Compressed sensing magnetic resonance imaging, Deep learning, Interpretable sampler learning, Implicit regularization learning BibRef

Morssy, A.[Amr], Frean, M.R.[Marcus R.], Teal, P.D.[Paul D.],
Informed Adaptive Sensing,
PAMI(46), No. 5, May 2024, pp. 3230-3241.
IEEE DOI 2404
Inverse problems, Entropy, Adaptation models, Mutual information, Deep learning, Training, Numerical models, Experimental design, probabilistic algorithms BibRef

Liu, X.H.[Xiao-Han], Pang, Y.W.[Yan-Wei], Sun, X.B.[Xue-Bin], Liu, Y.M.[Yi-Ming], Hou, Y.H.[Yong-Hong], Wang, Z.C.[Zhen-Chang], Li, X.L.[Xue-Long],
Image Reconstruction for Accelerated MR Scan With Faster Fourier Convolutional Neural Networks,
IP(33), 2024, pp. 2966-2978.
IEEE DOI 2405
Image reconstruction, Convolution, Magnetic resonance imaging, Feature extraction, Task analysis, Interpolation, faster Fourier convolution BibRef

Polsinelli, M.[Matteo], Li, H.W.B.[Hong-Wei Bran], Mignosi, F.[Filippo], Zhang, L.[Li], Placidi, G.[Giuseppe],
Siamese network to assess scanner-related contrast variability in MRI,
IVC(145), 2024, pp. 104997.
Elsevier DOI 2405
MRI, MRI pre-processing, Deep learning, Siamese network, Explainable AI BibRef

Li, Z.M.[Zi-Meng], Xiao, S.[Sa], Wang, C.[Cheng], Li, H.D.[Hai-Dong], Zhao, X.C.[Xiu-Chao], Duan, C.H.[Cao-Hui], Zhou, Q.[Qian], Rao, Q.C.[Qiu-Chen], Fang, Y.[Yuan], Xie, J.S.[Jun-Shuai], Shi, L.[Lei], Guo, F.M.[Fu-Min], Ye, C.H.[Chao-Hui], Zhou, X.[Xin],
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI,
MedImg(43), No. 5, May 2024, pp. 1828-1840.
IEEE DOI 2405
Convolution, Magnetic resonance imaging, Image reconstruction, Lung, Kernel, Feature extraction, Encoding, Convolution kernel, lung, MRI reconstruction BibRef

Cao, C.T.[Chen-Tao], Cui, Z.X.[Zhuo-Xu], Wang, Y.[Yue], Liu, S.[Shaonan], Chen, T.[Taijin], Zheng, H.R.[Hai-Rong], Liang, D.[Dong], Zhu, Y.J.[Yan-Jie],
High-Frequency Space Diffusion Model for Accelerated MRI,
MedImg(43), No. 5, May 2024, pp. 1853-1865.
IEEE DOI Code:
WWW Link. 2405
Image reconstruction, Diffusion processes, Convergence, Mathematical models, Magnetic resonance imaging, inverse problem BibRef

Guan, Y.[Yu], Yu, C.M.[Chuan-Ming], Cui, Z.X.[Zhuo-Xu], Zhou, H.L.[Hui-Lin], Liu, Q.[Qiegen],
Correlated and Multi-Frequency Diffusion Modeling for Highly Under-Sampled MRI Reconstruction,
MedImg(43), No. 10, October 2024, pp. 3490-3502.
IEEE DOI 2411
Image reconstruction, Diffusion processes, Magnetic resonance imaging, Data models, Data mining, STEM, multi-frequency prior BibRef

Gan, H.P.[Hong-Ping], Wang, X.Y.[Xiao-Yang], He, L.J.[Li-Jun], Liu, J.[Jie],
Learned Two-Step Iterative Shrinkage Thresholding Algorithm for Deep Compressive Sensing,
CirSysVideo(34), No. 5, May 2024, pp. 3943-3956.
IEEE DOI 2405
Image reconstruction, Iterative algorithms, Sensors, Magnetic resonance imaging, Optimization, Transformers BibRef

Zhang, X.Z.[Xiao-Zhi], Zhou, L.[Liu], Wan, Y.P.[Ya-Ping], Ling, B.W.K.[Bingo Wing-Kuen], Xiong, D.P.[Dong-Ping],
TTMRI: Multislice texture transformer network for undersampled MRI reconstruction,
IET-IPR(18), No. 8, 2024, pp. 2126-2143.
DOI Link 2406
image reconstruction, magnetic resonance imaging, texture transfer, Transformer BibRef

Leynes, A.P.[Andrew P.], Deveshwar, N.[Nikhil], Nagarajan, S.S.[Srikantan S.], Larson, P.E.Z.[Peder E. Z.],
Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction,
MedImg(43), No. 6, June 2024, pp. 2358-2369.
IEEE DOI 2406
Coils, Magnetic resonance imaging, Deep learning, Image reconstruction, Sensitivity, Imaging, Compressed sensing, calibrationless MRI BibRef

Ding, Y.[Yue], Zhou, T.[Tao], Xiang, L.[Lei], Wu, Y.[Ye],
Cross-contrast mutual fusion network for joint MRI reconstruction and super-resolution,
PR(154), 2024, pp. 110599.
Elsevier DOI 2406
Magnetic resonance imaging, MRI reconstruction, Super-resolution, Multi-task learning BibRef

Li, Y.[Yanran], Chan, R.H.[Raymond H.], Shen, L.X.[Li-Xin], Zhuang, X.S.[Xiao-Sheng], Wu, R.S.[Ri-Sheng], Huang, Y.J.[Yi-Jun], Liu, J.W.[Jun-Wei],
Exploring Structural Sparsity of Coil Images from 3-Dimensional Directional Tight Framelets for SENSE Reconstruction,
SIIMS(17), No. 2, 2024, pp. 888-916.
DOI Link 2407
parallel magnetic resonance imaging (pMRI) system BibRef

Fang, F.[Faming], Hu, L.[Le], Liu, J.H.[Jin-Hao], Yi, Q.[Qiaosi], Zeng, T.Y.[Tie-Yong], Zhang, G.X.[Gui-Xu],
HFGN: High-Frequency residual Feature Guided Network for fast MRI reconstruction,
PR(156), 2024, pp. 110801.
Elsevier DOI 2408
Complex convolutional neural network, Deep learning, Fourier convolution, Image reconstruction, Magnetic resonance imaging BibRef

Ruan, G.H.[Guo-Hui], Wang, Z.[Zhaonian], Liu, C.[Chunyi], Xia, L.[Ling], Wang, H.F.[Hua-Feng], Qi, L.[Li], Chen, W.F.[Wu-Fan],
Magnetic Resonance Electrical Properties Tomography Based on Modified Physics- Informed Neural Network and Multiconstraints,
MedImg(43), No. 9, September 2024, pp. 3263-3278.
IEEE DOI 2409
Mathematical models, Magnetic resonance imaging, Radio frequency, Image reconstruction, Graphical models, Distribution functions, multiconstraints BibRef

Lei, P.C.[Peng-Cheng], Hu, L.[Le], Fang, F.[Faming], Zhang, G.X.[Gui-Xu],
Joint Under-Sampling Pattern and Dual-Domain Reconstruction for Accelerating Multi-Contrast MRI,
IP(33), 2024, pp. 4686-4701.
IEEE DOI Code:
WWW Link. 2409
Image reconstruction, Magnetic resonance imaging, Optimization, Task analysis, Image restoration, Feature extraction, under-sampling pattern optimization BibRef

Fahim, M.A.[Mohammad Al], Ramanarayanan, S.[Sriprabha], Rahul, G.S., Gayathri, M.N.[Matcha Naga], Sarkar, A.[Arunima], Ram, K.[Keerthi], Sivaprakasam, M.[Mohanasankar],
OCUCFormer: An Over-Complete Under-Complete Transformer Network for accelerated MRI reconstruction,
IVC(150), 2024, pp. 105228.
Elsevier DOI Code:
WWW Link. 2409
Convolutional RNN, Vision transformer, MRI reconstruction BibRef

Cui, Z.X.[Zhuo-Xu], Liu, C.C.[Cong-Cong], Fan, X.H.[Xiao-Hong], Cao, C.[Chentao], Cheng, J.[Jing], Zhu, Q.Y.[Qing-Yong], Liu, Y.Y.[Yuan-Yuan], Jia, S.[Sen], Wang, H.F.[Hai-Feng], Zhu, Y.J.[Yan-Jie], Zhou, Y.H.[Yi-Hang], Zhang, J.P.[Jian-Ping], Liu, Q.[Qiegen], Liang, D.[Dong],
Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion,
MedImg(43), No. 10, October 2024, pp. 3503-3520.
IEEE DOI Code:
WWW Link. 2411
Interpolation, Mathematical models, Diffusion models, Heating systems, Hafnium, Magnetic resonance imaging, Data models, physics-informed deep learning BibRef

Wisniewski, M.[Mariusz], Giulivi, L.[Loris], Boracchi, G.[Giacomo],
SE3D: A Framework for Saliency Method Evaluation in 3D Imaging,
ICIP24(89-95)
IEEE DOI 2411
Measurement, Deep learning, Solid modeling, Laser radar, Magnetic resonance imaging, Deep Learning, Saliency Maps, 3D Convolutions BibRef

Ito, S.[Satoshi], Sato, Y.[Yuki], Endo, N.[Naoya], Ouchi, S.[Shohei],
Deep-Learning-Based Magnetic Resonance Simultaneous Multislice Imaging Using Holographic Image Decoding,
ICIP24(2852-2857)
IEEE DOI 2411
Image quality, Deep learning, Phase modulation, Neural networks, Magnetic resonance, Holography, Amplitude modulation, convolutional neural network (CNN) BibRef

Xiao, M.[Min], Wang, Z.[Zi], Guo, J.F.[Jie-Feng], Guo, D.[Di], Qu, X.B.[Xiao-Bo],
A 1D Plug-and-Play Synthetic Data Deep Learning for Undersampled Magnetic Resonance Image Reconstruction,
ICIP24(2827-2832)
IEEE DOI 2411
Training, Deep learning, in vivo, Magnetic resonance imaging, Magnetic resonance, Training data, Trajectory, synthetic data BibRef

Yamato, K.[Kazuki], Ito, S.[Satoshi],
Improvement of Image Reconstruction for MRI Using Phase-Scrambling Fourier Transform and Dual-Domain Strategy,
ICIP24(2858-2864)
IEEE DOI 2411
Deep learning, Fourier transforms, Smoothing methods, Convolution, Magnetic resonance imaging, Noise reduction, Indium tin oxide, dual-domain strategy BibRef

Ibrahim, V.[Vazim], Paul, J.S.[Joseph Suresh],
A Cross Domain Generative Network for Accelerated MRI,
ICIP24(2865-2870)
IEEE DOI 2411
Extrapolation, Accuracy, Magnetic resonance imaging, Frequency-domain analysis, Neural networks, Estimation, Convolutional Neural Network BibRef

Reddy, K.P.K.[K. Pavan Kumar], Chaudhury, K.N.[Kunal N.],
Deep Regularization for Scale-Agnostic Superresolution of MR Images,
ICIP24(2820-2826)
IEEE DOI 2411
Technological innovation, Accuracy, Inverse problems, Magnetic resonance imaging, Superresolution, Magnetic resonance, superresolution BibRef

Ting, C.M.[Chee-Ming], Noman, F.[Fuad], Phan, R.C.W.[Raphaël C.W.], Ombao, H.[Hernando],
Dynamic MRI Reconstruction Using Low-Rank Plus Sparse Decomposition With Smoothness Regularization,
ICIP24(2800-2806)
IEEE DOI 2411
Accuracy, Tensors, Smoothing methods, Magnetic resonance imaging, Noise, Data models, Robustness, Dynamic MRI, low-rank, sparsity, proximal gradient BibRef

Li, G.Y.[Guang-Yuan], Rao, C.[Chen], Mo, J.C.[Jun-Cheng], Zhang, Z.J.[Zhan-Jie], Xing, W.[Wei], Zhao, L.[Lei],
Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution,
CVPR24(11365-11374)
IEEE DOI Code:
WWW Link. 2410
Magnetic resonance imaging, Face recognition, Superresolution, Reconstruction algorithms, Diffusion models, Transformers, super-resolution BibRef

Weber, T.[Tobias], Ingrisch, M.[Michael], Bischl, B.[Bernd], Rügamer, D.[David],
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction,
WACV24(7650-7659)
IEEE DOI 2404
Image segmentation, Protocols, Magnetic resonance imaging, PROM, Probabilistic logic, Trajectory, Task analysis, Applications BibRef

Kim, K.[Kyuri], Na, Y.[Yoonho], Ye, S.J.[Sung-Joon], Lee, J.[Jimin], Ahn, S.S.[Sung Soo], Park, J.E.[Ji Eun], Kim, H.[Hwiyoung],
Controllable Text-to-Image Synthesis for Multi-Modality MR Images,
WACV24(7921-7930)
IEEE DOI 2404
Image synthesis, Noise reduction, Magnetic resonance, Computer architecture, Solids, Data models, Applications, Vision + language and/or other modalities BibRef

Lei, P.C.[Peng-Cheng], Fang, F.[Faming], Zhang, G.X.[Gui-Xu], Zeng, T.Y.[Tie-Yong],
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction,
ICCV23(21239-21249)
IEEE DOI Code:
WWW Link. 2401
BibRef

Wang, Q.[Qi], Mahler, L.[Lucas], Steiglechner, J.[Juliu], Birk, F.[Florian], Scheffler, K.[Klaus], Lohmann, G.[Gabriele],
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning,
CVAMD23(2444-2453)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yu, S.C.[Shao-Cong], Chu, X.[Xueye], Zhou, Z.L.[Zheng-Lin], Guo, J.F.[Jie-Feng], Ding, X.H.[Xing-Hao],
Joint Under-Sampling Pattern Optimization and Content-Based Reconstruction Network for Fast MRI Reconstruction,
ICIP23(730-734)
IEEE DOI 2312
BibRef

Liu, J.[Jingshuai], Qin, C.[Chen], Yaghoobi, M.[Mehrdad],
Coil-agnostic Attention-based Network for Parallel MRI Reconstruction,
ACCV22(VI:168-184).
Springer DOI 2307
BibRef

Pramanik, A.[Aniket], Jacob, M.[Mathews],
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI,
MCV22(437-453).
Springer DOI 2304
BibRef

Wang, W.[Wanliang], Xing, F.[Fangsen], Chen, J.C.[Jia-Cheng], Tu, H.Y.[Hang-Yao],
Edge Assisted Asymmetric Convolution Network for MR Image Super-resolution,
MMMod23(II: 66-78).
Springer DOI 2304
BibRef

Zhou, B.[Bo], Dey, N.[Neel], Schlemper, J.[Jo], Salehi, S.S.M.[Seyed Sadegh Mohseni], Liu, C.[Chi], Duncan, J.S.[James S.], Sofka, M.[Michal],
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction,
WACV23(4955-4964)
IEEE DOI 2302
Training, Costs, Magnetic resonance imaging, Redundancy, Training data, Information sharing, image and video synthesis BibRef

Yamato, K.[Kazuki], Ito, S.[Satoshi],
Super-Resolution Magnetic Resonance Imaging using Segmented Signals in Phase-Scrambling Fourier Transform Imaging and Deep Learning,
ICIP22(2551-2555)
IEEE DOI 2211
Deep learning, Image segmentation, Fourier transforms, Magnetic resonance imaging, Superresolution, Signal sampling, deep learning BibRef

Wyatt, J.[Julian], Leach, A.[Adam], Schmon, S.M.[Sebastian M.], Willcocks, C.G.[Chris G.],
AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise,
NTIRE22(649-655)
IEEE DOI 2210
Training, Image segmentation, Shape, Magnetic resonance imaging, Gaussian noise, Noise reduction, Markov processes BibRef

Cole, E.[Elizabeth], Meng, Q.X.[Qing-Xi], Pauly, J.[John], Vasanawala, S.[Shreyas],
Learned Compression of High Dimensional Image Datasets,
CLIC22(1747-1751)
IEEE DOI 2210
Radio frequency, Photography, Image coding, Runtime, Magnetic resonance imaging BibRef

Peng, W.[Wei], Feng, L.[Li], Zhao, G.Y.[Guo-Ying], Liu, F.[Fang],
Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks,
CVPR22(20762-20771)
IEEE DOI 2210
Deep learning, Training, Magnetic resonance imaging, Magnetic resonance, Ordinary differential equations, Image and video synthesis and generation BibRef

Li, G.Y.[Guang-Yuan], Zhao, L.[Lei], Sun, J.[Jiakai], Lan, Z.[Zehua], Zhang, Z.J.[Zhan-Jie], Chen, J.[Jiafu], Lin, Z.J.[Zhi-Jie], Lin, H.Z.[Huai-Zhong], Xing, W.[Wei],
Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale Upsampling,
ICCV23(21173-21183)
IEEE DOI Code:
WWW Link. 2401
BibRef

Li, G.Y.[Guang-Yuan], Lv, J.[Jun], Tian, Y.[Yapeng], Dou, Q.[Qi], Wang, C.Y.[Cheng-Yan], Xu, C.L.[Chen-Liang], Qin, J.[Jing],
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution,
CVPR22(20604-20613)
IEEE DOI 2210
Codes, Fuses, Magnetic resonance imaging, Aggregates, Superresolution, Anatomical structure, Medical, Image and video synthesis and generation BibRef

Chung, H.[Hyungjin], Sim, B.[Byeongsu], Ye, J.C.[Jong Chul],
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction,
CVPR22(12403-12412)
IEEE DOI 2210
Inverse problems, Gaussian noise, Magnetic resonance imaging, Difference equations, Superresolution, Stochastic processes, biological and cell microscopy BibRef

Yiasemis, G.[George], Sonke, J.J.[Jan-Jakob], Sánchez, C.[Clarisa], Teuwen, J.[Jonas],
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction,
CVPR22(722-731)
IEEE DOI 2210
Deep learning, Recurrent neural networks, Inverse problems, Magnetic resonance imaging, Computer architecture, Task analysis, Others BibRef

Yan, Q.[Qiunv], Liu, L.[Li], Mei, L.[Lanyin],
Learning Unrolling-Based Neural Network for Magnetic Resonance Imaging Reconstruction,
CIAP22(I:124-136).
Springer DOI 2205
BibRef

Zabihi, S.[Soheil], Rahimian, E.[Elahe], Asif, A.[Amir], Mohammadi, A.[Arash],
SepUnet: Depthwise Separable Convolution Integrated U-Net for MRI Reconstruction,
ICIP21(3792-3796)
IEEE DOI 2201
Deep learning, Convolution, Magnetic resonance imaging, Computer architecture, Reconstruction algorithms, Atrous Spatial Pyramidal Pooling (ASPP) BibRef

Seo, H.S.[Hyun-Seok], Shin, K.M.[Kelly M.], Kyung, Y.[Yeunwoong],
A Dual Domain Network for MRI Reconstruction Using Gabor Loss,
ICIP21(146-149)
IEEE DOI 2201
Sensitivity, Magnetic resonance imaging, Receivers, Reconstruction algorithms, Filtering algorithms, Neural networks BibRef

Hernandez, A.G.[Armando Garcia], Fau, P.[Pierre], Rapacchi, S.[Stanislas], Wojak, J.[Julien], Mailleux, H.[Hugues], Benkreira, M.[Mohamed], Adel, M.[Mouloud],
Improving Image Quality In Low-Field MRI With Deep Learning,
ICIP21(260-263)
IEEE DOI 2201
Image quality, Deep learning, Image segmentation, Magnetic resonance imaging, Noise reduction, Transfer learning, AutoEncoder BibRef

Cole, E.K.[Elizabeth K.], Ong, F.[Frank], Vasanawala, S.S.[Shreyas S.], Pauly, J.M.[John M.],
Fast Unsupervised MRI Reconstruction Without Fully-Sampled Ground Truth Data Using Generative Adversarial Networks,
LCI21(3971-3980)
IEEE DOI 2112
Deep learning, Training, Measurement, Magnetic resonance imaging, Data acquisition BibRef

Ryu, K.[Kanghyun], Alkan, C.[Cagan], Choi, C.[Chanyeol], Jang, I.[Ikbeom], Vasanawala, S.[Shreyas],
K-space refinement in deep learning MR reconstruction via regularizing scan specific SPIRiT-based self consistency,
LCI21(3991-4000)
IEEE DOI 2112
Deep learning, Magnetic resonance imaging, Measurement uncertainty, Neural networks, Iterative methods BibRef

Ekmekci, C.[Canberk], Cetin, M.[Mujdat],
What Does Your Computational Imaging Algorithm Not Know?: A Plug-and-Play Model Quantifying Model Uncertainty,
LCI21(4001-4010)
IEEE DOI 2112
Training, Deep learning, Uncertainty, Computational modeling, Magnetic resonance imaging, Training data, Data models BibRef

Gan, W.J.[Wei-Jie], Hu, Y.Y.[Yu-Yang], Eldeniz, C.[Cihat], Liu, J.M.[Jia-Ming], Chen, Y.S.[Ya-Sheng], An, H.Y.[Hong-Yu], Kamilov, U.S.[Ulugbek S.],
SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI without Ground Truth,
LCI21(4031-4039)
IEEE DOI 2112
Coils, Image quality, Sensitivity, Magnetic resonance imaging, Noise reduction, Estimation, Receivers BibRef

Guo, P.F.[Peng-Fei], Wang, P.[Puyang], Zhou, J.Y.[Jin-Yuan], Jiang, S.S.[Shan-Shan], Patel, V.M.[Vishal M.],
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning,
CVPR21(2423-2432)
IEEE DOI 2111
Data privacy, Protocols, Collaboration, Magnetic resonance, Sensor phenomena and characterization, Collaborative work, Data models BibRef

Zhang, Y.L.[Yu-Lun], Wei, D.L.[Dong-Lai], Qin, C.[Can], Wang, H.[Huan], Pfister, H.[Hanspeter], Fu, Y.[Yun],
Context Reasoning Attention Network for Image Super-Resolution,
ICCV21(4258-4267)
IEEE DOI 2203
Degradation, Adaptation models, Neuroscience, Convolution, Superresolution, Semantics, Feature extraction, BibRef

Zhang, Y.L.[Yu-Lun], Li, K.[Kai], Li, K.P.[Kun-Peng], Fu, Y.[Yun],
MR Image Super-Resolution with Squeeze and Excitation Reasoning Attention Network,
CVPR21(13420-13429)
IEEE DOI 2111
Training, Visualization, Adaptation models, Computational modeling, Superresolution, Cognition BibRef

Jun, Y.[Yohan], Shin, H.[Hyungseob], Eo, T.[Taejoon], Hwang, D.[Dosik],
Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI,
CVPR21(5266-5275)
IEEE DOI 2111
Deep learning, Sensitivity, Image resolution, Magnetic resonance imaging, Magnetic resonance, Network architecture BibRef

Pokala, P.K., Chemudupati, S., Seelamantula, C.S.,
Generalized Fast Iteratively Reweighted Soft-Thresholding Algorithm for Sparse Coding Under Tight Frames in the Complex-Domain,
ICIP20(2875-2879)
IEEE DOI 2011
Image reconstruction, Magnetic resonance imaging, Convergence, Transforms, Optimization, Smoothing methods, Magnetic resonance, tight frames BibRef

Millard, C., Hess, A.T., Mailhe, B., Tanner, J.,
An Approximate Message Passing Algorithm For Rapid Parameter-Free Compressed Sensing MRI,
ICIP20(91-95)
IEEE DOI 2011
Approximation algorithms, Magnetic resonance imaging, Compressed sensing, Sensors, Image reconstruction, Standards, Magnetic Resonance Imaging BibRef

Sriram, A., Zbontar, J., Murrell, T., Zitnick, C.L., Defazio, A., Sodickson, D.K.,
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction,
CVPR20(14303-14310)
IEEE DOI 2008
Image reconstruction, Coils, Magnetic resonance imaging, Acceleration, Mathematical model, Machine learning BibRef

Zhou, B., Zhou, S.K.,
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction With Deep T1 Prior,
CVPR20(4272-4281)
IEEE DOI 2008
Magnetic resonance imaging, Image restoration, Image reconstruction, Feature extraction, Protocols, Acceleration BibRef

Vasudeva, B.[Bhavya], Deora, P.[Puneesh], Bhattacharya, S.[Saumik], Pradhan, P.M.[Pyari Mohan],
Compressed Sensing MRI Reconstruction with Co-VeGAN: Complex-Valued Generative Adversarial Network,
WACV22(1779-1788)
IEEE DOI 2202
BibRef
Earlier: A2, A1, A3, A4:
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks,
NTIRE20(2211-2219)
IEEE DOI 2008
Learning systems, Magnetic resonance imaging, Generative adversarial networks, Data models, Data mining, Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy. Image reconstruction, Generators, Training, Magnetic resonance imaging BibRef

Pour Yazdanpanah, A., Afacan, O., Warfield, S.,
Deep Plug-and-Play Prior for Parallel MRI Reconstruction,
CLI19(3952-3958)
IEEE DOI 2004
Image reconstruction, Magnetic resonance imaging, Sensitivity, Acceleration, Mathematical model, Gold, Parallel imaging, compressed sensing BibRef

He, S., Jalali, B.,
Fast Super-Resolution in MRI Images Using Phase Stretch Transform, Anchored Point Regression and Zero-Data Learning,
ICIP19(2876-2880)
IEEE DOI 1910
Medical image enhancement, Zero-data learning, Phase stretch transform, Fast inference, Edge computing BibRef

Zhuang, P., Ding, X.,
Compressed Sensing MRI with Joint Image-Level and Patch-Level Priors,
ICIP19(2080-2084)
IEEE DOI 1910
MRI reconstruction, total variation, expected patch log likelihood, alternative optimization BibRef

Gu, X.[Xuan], Knutsson, H.[Hans], Nilsson, M.[Markus], Eklund, A.[Anders],
Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks,
SCIA19(489-498).
Springer DOI 1906
BibRef

Kaur, P., Sharma, A., Nigam, A., Bhavsar, A.,
MR-Srnet: Transformation of Low Field MR Images to High Field MR Images,
ICIP18(2057-2061)
IEEE DOI 1809
Image reconstruction, Decoding, Training, Neural networks, Training data, Task analysis, Merge connections BibRef

Jiang, Q., Moussaoui, S., Idier, J., Collewet, G., Xu, M.,
Majorization-minimization algorithms for maximum likelihood estimation of magnetic resonance images,
IPTA17(1-6)
IEEE DOI 1804
gradient methods, image restoration, maximum likelihood estimation, minimisation, MM algorithms, maximum likelihood estimation BibRef

Gopal, P., Bailey, D., Svalbe, I.,
Nonlinear Interpolation in the Fourier Domain Guided by Morphologic Filters,
DICTA17(1-8)
IEEE DOI 1804
Fourier analysis, biomedical MRI, compressed sensing, data acquisition, frequency-domain analysis, image filtering, Shape BibRef

Sun, L., Huang, Y., Cai, C., Ding, X.,
Compressed sensing MRI using total variation regularization with K-space decomposition,
ICIP17(3061-3065)
IEEE DOI 1803
Frequency measurement, Image reconstruction, Linear programming, Magnetic resonance imaging, Optimization, TV, Total Variation BibRef

Chaabene, S., Chaari, L.,
Bayesian myopic parallel MRI reconstruction,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Diffusion Tensor Imaging .

Last update:Nov 26, 2024 at 16:40:19