20.11.1 Magnetic Resonance Imaging Systems, Hardware Implementations

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

Arduino, A., Zilberti, L., Chiampi, M., Bottauscio, O.,
CSI-EPT in Presence of RF-Shield for MR-Coils,
MedImg(36), No. 7, July 2017, pp. 1396-1404.
IEEE DOI 1707
Magnetic domains, Magnetic properties, Magnetic resonance imaging, Mathematical model, Minimization, Numerical models, Radio frequency, Contrast source inversion, Maxwell equations, electric properties tomography, finite element method, magnetic resonance imaging, specific, absorption, rate 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, pseudoGibbs artifacts, redundant shift-invariant wavelet transform, signal reconstruction, sparse signal recovery, sparse signal representation, Discrete wavelet transforms, Image reconstruction, Noise reduction, Redundancy, Signal to noise ratio, Spinning, Compressed sensing, MRI, cycle spinning, dictionary splitting, 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

Honarvar, M., Sahebjavaher, R.S., Rohling, R., Salcudean, S.E.,
A Comparison of Finite Element-Based Inversion Algorithms, Local Frequency Estimation, and Direct Inversion Approach Used in MRE,
MedImg(36), No. 8, August 2017, pp. 1686-1698.
IEEE DOI 1708
Elasticity, Elastography, Filter banks, Finite element analysis, Frequency estimation, Inverse problems, Mathematical model, Dynamic elastography, finite element method, inverse problem, magnetic resonance elastography, medical imaging, sparsity, regularization 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.[Yifei], 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

Cheng, J., Shen, D., Yap, P.T., Basser, P.J.,
Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes,
MedImg(37), No. 1, January 2018, pp. 185-199.
IEEE DOI 1801
biodiffusion, biological tissues, biomedical MRI, codes, image coding, image reconstruction, image sampling, uniform spherical sampling 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

Kroboth, S., Layton, K.J., Jia, F., Littin, S., Yu, H., Hennig, J., Zaitsev, M.,
Optimization of Coil Element Configurations for a Matrix Gradient Coil,
MedImg(37), No. 1, January 2018, pp. 284-292.
IEEE DOI 1801
biomedical MRI, brain, simulated annealing, 84-channel matrix gradient coil, NP-hard combinatorial nature, simulated annealing 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

Horneff, A., Eder, M., Hell, E., Ulrici, J., Felder, J., Rasche, V., Anders, J.,
An EM Simulation-Based Design Flow for Custom-Built MR Coils Incorporating Signal and Noise,
MedImg(37), No. 2, February 2018, pp. 527-535.
IEEE DOI 1802
Coils, Phantoms, Prototypes, Sensitivity, Signal to noise ratio, MR SNR simulation, MR coil simulation, MR image SNR simulation, Software-based MR coil design 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

Rund, A., Aigner, C.S., Kunisch, K., Stollberger, R.,
Magnetic Resonance RF Pulse Design by Optimal Control With Physical Constraints,
MedImg(37), No. 2, February 2018, pp. 461-472.
IEEE DOI 1802
Magnetization, Mathematical model, Newton method, Optimal control, Optimization, Radio frequency, RF pulse design, slice-selective BibRef

Marjanovic, J., Weiger, M., Reber, J., Brunner, D.O., Dietrich, B.E., Wilm, B.J., Froidevaux, R., Pruessmann, K.P.,
Multi-Rate Acquisition for Dead Time Reduction in Magnetic Resonance Receivers: Application to Imaging With Zero Echo Time,
MedImg(37), No. 2, February 2018, pp. 408-416.
IEEE DOI 1802
Filter banks, Finite impulse response filters, Imaging, Kernel, Radio frequency, Filter cascade, MRI, ZTE, multi-rate acquisition, short T2 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

Zimmermann, M.[Markus], Abbas, Z.[Zaheer], Dzieciol, K.[Krzysztof], Shah, N.J.[N. Jon],
Accelerated Parameter Mapping of Multiple-Echo Gradient-Echo Data Using Model-Based Iterative Reconstruction,
MedImg(37), No. 2, February 2018, pp. 626-637.
IEEE DOI 1802
MIRAGE. Data models, Image reconstruction, Iterative methods, Magnetomechanical effects, Solid modeling, Time series analysis, parameter mapping 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., Yu, S., Dong, H., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., Firmin, D.,
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, Gallium nitride, Image quality, Image reconstruction, Machine learning, Magnetic resonance imaging, Training, MRI BibRef

Eroglu, H.H., Sadighi, M., Eyüboglu, B.M.,
Induced Current Magnetic Resonance Electrical Conductivity Imaging With Oscillating Gradients,
MedImg(37), No. 7, July 2018, pp. 1606-1617.
IEEE DOI 1808
biomedical MRI, eddy currents, electric impedance imaging, electrical conductivity, image reconstruction, magnetic flux, magnetic resonance imaging BibRef

Ha, Y., Choi, C., Shah, N.J.,
Development and Implementation of a PIN-Diode Controlled, Quadrature-Enhanced, Double-Tuned RF Coil for Sodium MRI,
MedImg(37), No. 7, July 2018, pp. 1626-1631.
IEEE DOI 1808
biomedical MRI, coils, p-i-n diodes, single-tuned coils, double-tuned RF coil, sodium MRI, MR-detectable sodium signals, high field 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

Li, Q., Liao, C., Ye, H., Chen, Y., Cao, X., Yuan, L., He, H., Zhong, J.,
Squeezed Trajectory Design for Peak RF and Integrated RF Power Reduction in Parallel Transmission MRI,
MedImg(37), No. 8, August 2018, pp. 1809-1821.
IEEE DOI 1808
Radio frequency, Trajectory, Acceleration, Shape, Magnetic resonance imaging, Spirals, MRI, RF pulse design, particle swarm optimization BibRef

Gao, Y., Chen, W., Zhang, X.,
Investigating the Influence of Spatial Constraints on Ultimate Receive Coil Performance for Monkey Brain MRI at 7 T,
MedImg(37), No. 7, July 2018, pp. 1723-1732.
IEEE DOI 1808
biomedical MRI, brain, coils, medical image processing, coil-to-object distance, coil elements layout, ultra-high field (UHF) BibRef

Eksioglu, E., Tanc, A.,
Denoising AMP for MRI Reconstruction: BM3D-AMP-MRI,
SIIMS(11), No. 3, 2018, pp. 2090-2109.
DOI Link 1810
BibRef

Chen, Q., Xie, G., Luo, C., Yang, X., Zhu, J., Lee, J., Su, S., Liang, D., Zhang, X., Liu, X., Li, Y., Zheng, H.,
A Dedicated 36-Channel Receive Array for Fetal MRI at 3T,
MedImg(37), No. 10, October 2018, pp. 2290-2297.
IEEE DOI 1810
Coils, Magnetic resonance imaging, Signal to noise ratio, Pregnancy, Acceleration, Abdomen, Fetus imaging, radio-frequency (RF) coil BibRef

Chandra, S.S., Ruben, G., Jin, J., Li, M., Kingston, A.M., Svalbe, I.D., Crozier, S.,
Chaotic Sensing,
IP(27), No. 12, December 2018, pp. 6079-6092.
IEEE DOI 1810
biomedical MRI, chaos, discrete Fourier transforms, fractals, image denoising, image reconstruction, image sampling, compressed sensing 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

Hong, S., Choi, C., Magill, A.W., Jon Shah, N., Felder, J.,
Design of a Quadrature 1H/31P Coil Using Bent Dipole Antenna and Four-Channel Loop at 3T MRI,
MedImg(37), No. 12, December 2018, pp. 2613-2618.
IEEE DOI 1812
Dipole antennas, Antenna arrays, Antenna measurements, Sensitivity, Phantoms, Signal to noise ratio, Head, Magnetic resonance imaging, quadrature bent dipole antenna BibRef

Yang, Y.Y.[Yun-Yun], Qin, X.[Xuxu], Wu, B.[Boying],
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

Dixit, N., Stang, P.P., Pauly, J.M., Scott, G.C.,
Thermo-Acoustic Ultrasound for Detection of RF-Induced Device Lead Heating in MRI,
MedImg(37), No. 2, February 2018, pp. 536-546.
IEEE DOI 1802
Acoustics, Frequency modulation, Heating systems, Lead, Radio frequency, Signal to noise ratio, Implanted devices, thermoacoustic imaging 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

Yeh, J.T., Lin, J.L., Li, Y., Lin, F.,
A Flexible and Modular Receiver Coil Array for Magnetic Resonance Imaging,
MedImg(38), No. 3, March 2019, pp. 824-833.
IEEE DOI 1903
Receivers, Signal to noise ratio, Magnetic resonance imaging, Phantoms, Electromagnetics, Capacitors, Circumferential shielding, mutual coupling 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

Carrillo, H., Osses, A., Uribe, S., Bertoglio, C.,
Optimal Dual-VENC Unwrapping in Phase-Contrast MRI,
MedImg(38), No. 5, May 2019, pp. 1263-1270.
IEEE DOI 1905
Velocity measurement, Phase measurement, Magnetic resonance imaging, Encoding, Estimation, unwrapping BibRef

Mäkinen, A.J., Zevenhoven, K.C.J., Ilmoniemi, R.J.,
Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG-MRI,
MedImg(38), No. 6, June 2019, pp. 1317-1327.
IEEE DOI 1906
Magnetic resonance imaging, Sensor arrays, Calibration, Coils, Magnetic sensors, Sensitivity, Calibration, co-registration, ULF MRI 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

Mehmann, A., Vogt, C., Varga, M., Port, A., Reber, J., Marjanovic, J., Pruessmann, K.P., Sporrer, B., Huang, Q., Tröster, G.,
Automatic Resonance Frequency Retuning of Stretchable Liquid Metal Receive Coil for Magnetic Resonance Imaging,
MedImg(38), No. 6, June 2019, pp. 1420-1426.
IEEE DOI 1906
Resonant frequency, Signal to noise ratio, Magnetic resonance imaging, Receivers, Strain, stretchable 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

Haldar, J.P., Kim, D.,
OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI,
MedImg(38), No. 7, July 2019, pp. 1545-1558.
IEEE DOI 1907
Magnetic resonance imaging, Image reconstruction, Covariance matrices, Data acquisition, Image coding, Data models, compressed sensing BibRef

Zhang, J., Wu, J., Chen, S., Zhang, Z., Cai, S., Cai, C., Chen, Z.,
Robust Single-Shot T2 Mapping via Multiple Overlapping-Echo Acquisition and Deep Neural Network,
MedImg(38), No. 8, August 2019, pp. 1801-1811.
IEEE DOI 1908
Magnetic resonance imaging, Image reconstruction, Acceleration, Organic light emitting diodes, Weight measurement, Convolution, single shot BibRef

Speidel, T., Metze, P., Rasche, V.,
Efficient 3D Low-Discrepancy k-Space Sampling Using Highly Adaptable Seiffert Spirals,
MedImg(38), No. 8, August 2019, pp. 1833-1840.
IEEE DOI 1908
Spirals, Trajectory, Jacobian matrices, Magnetic resonance imaging, Acceleration, 3D, spiral, low-discrepancy 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

Schomburg, H., Hohage, T.,
Formulation and Efficient Computation of L_1 - and Smoothness Penalized Estimates for Microstructure-Informed Tractography,
MedImg(38), No. 8, August 2019, pp. 1899-1909.
IEEE DOI 1908
Convex functions, Integrated circuits, Magnetic resonance imaging, ISO, Image reconstruction, white matter microstructure BibRef

Ilbey, S., Top, C.B., Güngör, A., Çukur, T., Saritas, E.U., Güven, H.E.,
Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging,
MedImg(38), No. 9, September 2019, pp. 2070-2080.
IEEE DOI 1909
Calibration, Image reconstruction, Position measurement, Signal to noise ratio, Standards, Biomedical imaging, calibration 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, Gallium nitride, 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

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

Sandino, C.M., Cheng, J.Y., Chen, F., Mardani, M., Pauly, J.M., Vasanawala, S.S.,
Compressed Sensing: From Research to Clinical Practice With Deep Neural Networks: Shortening Scan Times for Magnetic Resonance Imaging,
SPMag(37), No. 1, January 2020, pp. 117-127.
IEEE DOI 2001
Image reconstruction, Magnetic resonance imaging, Neural networks, Transforms, Compressed sensing, Sensitivity 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

Guo, L., Li, M., Nguyen, P., Liu, F., Crozier, S.,
Integral MR-EPT With the Calculation of Coil Current Distributions,
MedImg(39), No. 1, January 2020, pp. 175-187.
IEEE DOI 2001
Current distribution, Mathematical model, Radio frequency, Image reconstruction, Integral equations, Load modeling, specific absorption rate BibRef

Eder, M., Horneff, A., Paul, J., Storm, A., Wunderlich, A., Hell, E., Ulrici, J., Anders, J., Rasche, V.,
A Signal Acquisition Setup for Ultrashort Echo Time Imaging Operating in Parallel on Unmodified Clinical MRI Scanners Achieving an Acquisition Delay of 3-mu-s ,
MedImg(39), No. 1, January 2020, pp. 218-225.
IEEE DOI 2001
Magnetic resonance imaging, Radio frequency, Delays, Switches, Synchronization, Field programmable gate arrays, MR data acquisition BibRef

Lee, H., Chung, J.J., Lee, J., Kim, S., Han, J., Park, J.,
Model-Based Chemical Exchange Saturation Transfer MRI for Robust z-Spectrum Analysis,
MedImg(39), No. 2, February 2020, pp. 283-293.
IEEE DOI 2002
Magnetic resonance imaging, Radio frequency, Radiation effects, Frequency measurement, Analytical models, Chemicals, compressed sensing 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

Heo, P.[Phil], Kim, H.J.[Han-Joong], Han, S.D.[Sang-Doc], Kim, D.[Donghyuk], Im, G.H.[Geun Ho], Kim, S.[Soobum], Kim, K.N.[Kyoung-Nam],
A study on multiple array method of birdcage coils to improve the signal intensity and homogeneity in small-animal whole-body magnetic resonance imaging at 7?T,
IJIST(30), No. 1, 2020, pp. 31-44.
DOI Link 2002
birdcage, EM simulation, magnetic resonance imaging BibRef

Hernandez, D.[Daniel], Seo, J.H.[Jeung-Hoon], Kim, K.N.[Kyoung-Nam],
Linear array arrangement using composite right-/left-handed transmission lines for magnetic resonance imaging,
IJIST(30), No. 1, 2020, pp. 216-223.
DOI Link 2002
electromagnetics, finite-element modeling, magnetic imaging, metamaterials, MRI, RF array coil 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

Wilm, B.J., Dietrich, B.E., Reber, J., Vannesjo, S.J., Pruessmann, K.P.,
Gradient Response Harvesting for Continuous System Characterization During MR Sequences,
MedImg(39), No. 3, March 2020, pp. 806-815.
IEEE DOI 2004
Probes, Magnetic resonance imaging, Nuclear magnetic resonance, Time measurement, Eddy currents, Linear systems, real-time 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

Marjanovic, J., Reber, J., Brunner, D.O., Engel, M., Kasper, L., Dietrich, B.E., Vionnet, L., Pruessmann, K.P.,
A Reconfigurable Platform for Magnetic Resonance Data Acquisition and Processing,
MedImg(39), No. 4, April 2020, pp. 1138-1148.
IEEE DOI 2004
Optical fiber sensors, Clocks, Real-time systems, Optical fibers, Imaging, Field programmable gate arrays, Real-time processing, expanded encoding model BibRef

Issa, I., Ford, K.L., Rao, M., Wild, J.M.,
A Magnetic Resonance Imaging Surface Coil Transceiver Employing a Metasurface for 1.5T Applications,
MedImg(39), No. 4, April 2020, pp. 1085-1093.
IEEE DOI 2004
Magnetic resonance imaging, Surface impedance, Radio frequency, Impedance, Phantoms, Frequency selective surfaces, metasurfaces BibRef

Reber, J., Marjanovic, J., Brunner, D.O., Port, A., Schmid, T., Dietrich, B.E., Moser, U., Barmet, C., Pruessmann, K.P.,
An In-Bore Receiver for Magnetic Resonance Imaging,
MedImg(39), No. 4, April 2020, pp. 997-1007.
IEEE DOI 2004
Magnetic resonance imaging, Radio frequency, Optical imaging, Optical receivers, Coils, In-bore electronics, MRI receiver, magnetic field monitoring 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

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

Zeng, W., Gordon-Wylie, S.W., Tan, L., Solamen, L., McGarry, M.D.J., Weaver, J.B., Paulsen, K.D.,
Nonlinear Inversion MR Elastography With Low-Frequency Actuation,
MedImg(39), No. 5, May 2020, pp. 1775-1784.
IEEE DOI 2005
Elastography, Mechanical factors, Mathematical model, Image reconstruction, Finite element analysis, Phantoms, viscoelastic modeling 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

Pawar, K.[Kamlesh], Chen, Z.L.[Zhao-Lin], Zhang, J.X.[Jing-Xin], Shah, N.J.[N. Jon], Egan, G.F.[Gary F.],
Application of compressed sensing using chirp encoded 3D GRE and MPRAGE sequences,
IJIST(30), No. 3, 2020, pp. 592-604.
DOI Link 2008
compressed sensing, image reconstruction, MRI pulse sequence BibRef

Chen, W., Lee, B.Y., Zhu, X.H., Wiesner, H.M., Sarkarat, M., Gandji, N.P., Rupprecht, S., Yang, Q.X., Lanagan, M.T.,
Tunable Ultrahigh Dielectric Constant (tuHDC) Ceramic Technique to Largely Improve RF Coil Efficiency and MR Imaging Performance,
MedImg(39), No. 10, October 2020, pp. 3187-3197.
IEEE DOI 2010
Iron, FCC, IP networks, Indexes, Magnetic resonance (MR) imaging (MRI), ultrahigh field (UHF) BibRef

Fantasia, M., Galante, A., Maggiorelli, F., Retico, A., Fontana, N., Monorchio, A., Alecci, M.,
Numerical and Workbench Design of 2.35 T Double-Tuned (H/Na) Nested RF Birdcage Coils Suitable for Animal Size MRI,
MedImg(39), No. 10, October 2020, pp. 3175-3186.
IEEE DOI 2010
Radio frequency, Coils, Magnetic resonance imaging, Couplings, Finite element analysis, Legged locomotion, MRI, decoupling 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

Eberhardt, B., Poser, B.A., Shah, N.J., Felder, J.,
Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI,
MedImg(39), No. 12, December 2020, pp. 4225-4236.
IEEE DOI 2012
Optimization, Radio frequency, Magnetic resonance imaging, Trajectory, Neuroscience, Nonhomogeneous media, Hardware, optimization BibRef

Sherry, F., Benning, M., de los Reyes, J.C., Graves, M.J., Maierhofer, G., Williams, G., Schönlieb, C.B., Ehrhardt, M.J.,
Learning the Sampling Pattern for MRI,
MedImg(39), No. 12, December 2020, pp. 4310-4321.
IEEE DOI 2012
Image reconstruction, Magnetic resonance imaging, Compressed sensing, Reconstruction algorithms, Training, regularisation BibRef

Shin, D.M.[Dong-Myung], Ji, S.[Sooyeon], Lee, D.[Doohee], Lee, J.[Jieun], Oh, S.H.[Se-Hong], Lee, J.[Jongho],
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR,
MedImg(39), No. 12, December 2020, pp. 4391-4400.
IEEE DOI 2012
Radio frequency, Reinforcement learning, Neural networks, Magnetic resonance imaging, Deep learning, Games, Optimization, AI design 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

Davids, M., Guérin, B., Klein, V., Wald, L.L.,
Optimization of MRI Gradient Coils With Explicit Peripheral Nerve Stimulation Constraints,
MedImg(40), No. 1, January 2021, pp. 129-142.
IEEE DOI 2012
Coils, Magnetic resonance imaging, Optimization, Windings, Current density, Torque, Gradient coil design, MRI safety, neurodynamic nerve model 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

Su, S., Qiu, Z., Luo, C., Shi, C., Wan, L., Zhu, Y., Li, Y., Liu, X., Zheng, H., Liang, D., Wang, H.,
Accelerated 3D bSSFP Using a Modified Wave-CAIPI Technique With Truncated Wave Gradients,
MedImg(40), No. 1, January 2021, pp. 48-58.
IEEE DOI 2012
Acceleration, Magnetic resonance imaging, Trajectory, CAIPIRINHA 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

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

Deora, P., Vasudeva, B., Bhattacharya, S., Pradhan, P.M.,
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks,
NTIRE20(2211-2219)
IEEE DOI 2008
Image reconstruction, Generators, Training, Magnetic resonance imaging, Generative adversarial networks 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

Zheng, S., Li, X., Yan, W.Q., Xu, W.,
Gradient Coils Design with Regularization Method for Superconducting Magnetic Resonance Imaging,
IVCNZ18(1-5)
IEEE DOI 1902
Coils, Magnetic resonance imaging, Magnetic noise, Magnetic shielding, Current density, Superconducting magnets, Tikhonov regularization 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,
ISIVC16(103-108)
IEEE DOI 1704
Bayes methods BibRef

Ulas, C.[Cagdas], Gómez, P.A.[Pedro A.], Krahmer, F.[Felix], Sperl, J.I.[Jonathan I.], Menzel, M.I.[Marion I.], Menze, B.H.[Bjoern H.],
Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints,
RAMBO16(37-47).
Springer DOI 1703
BibRef

Bones, P., King, L., Millane, R.P.,
MR imaging near metal: The POP algorithm,
ICVNZ16(1-6)
IEEE DOI 1701
Fats BibRef

Zhu, K., Zhang, C., Zhang, J.,
Coherence Analysis of Compressive Sensing Based Magnetic Resonance Imaging Reconstruction,
DICTA16(1-7)
IEEE DOI 1701
Coherence BibRef

Chandra, S.S., Archchige, R., Ruben, G., Jin, J., Li, M., Kingston, A.M., Svalbe, I., Crozier, S.,
Finite Radial Reconstruction for Magnetic Resonance Imaging: A Theoretical Study,
DICTA16(1-6)
IEEE DOI 1701
Discrete Fourier transforms BibRef

Jin, K.H., Lee, D., Lee, J., Ye, J.C.,
Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation,
ICIP16(968-972)
IEEE DOI 1610
Acceleration BibRef

Zhao, Q., Meng, D., Kong, X., Xie, Q., Cao, W., Wang, Y., Xu, Z.,
A Novel Sparsity Measure for Tensor Recovery,
ICCV15(271-279)
IEEE DOI 1602
Brain modeling BibRef

Rich, A.[Adam], Potter, L.C.[Lee C.], Ashi, J.N.[Joshua N.], Ahmad, R.[Rizwan],
Factor graphs for inverse problems: Accelerated phase contrast magnetic resonance imaging,
ICIP15(1026-1030)
IEEE DOI 1512
BibRef

Portejoie, P.[Pierre], Mure, S.[Simon], Benoit-Cattin, H.[Hugues], Grenier, T.[Thomas],
Locally controlled regularized spatiotemporal anisotropic diffusion,
ICIP15(4823-4827)
IEEE DOI 1512
Anisotropic Diffusion; Image Sequence Filtering; MRI; Regularization BibRef

Omer, O.A.[Osama A.], Bassiouny, M.A.[M. Atef], Morooka, K.[Ken'ichi],
Efficient Resolution Enhancement Algorithm for Compressive Sensing Magnetic Resonance Image Reconstruction,
CIAP15(I:519-527).
Springer DOI 1511
BibRef

Tarquino, J.[Jonathan], Rueda, A.[Andrea], Romero, E.[Eduardo],
Shearlet-based sparse representation for super-resolution in diffusion weighted imaging (DWI),
ICIP14(3897-3900)
IEEE DOI 1502
Dictionaries BibRef

Sun, H.[Hao], Grissom, W.A.[William A.], Fessler, J.A.[Jeffrey A.],
Regularized estimation of Bloch-Siegert B1-maps in MRI,
ICIP14(3646-3650)
IEEE DOI 1502
Algorithm design and analysis BibRef

Muckley, M.J.[Matthew J.], Fessler, J.A.[Jeffrey A.],
Fast MR image reconstruction with orthogonal wavelet regularization via shift-variant shrinkage,
ICIP14(3651-3655)
IEEE DOI 1502
Convergence BibRef

Zhang, M.Q.[Mei-Qing], Nie, H.[Huirao], Pei, Y.[Yang], Tao, L.M.[Lin-Mi],
Volume Reconstruction for MRI,
ICPR14(3351-3356)
IEEE DOI 1412
Dictionaries BibRef

Izydorczyk, G.[Grzegorz], Klepaczko, A.[Artur],
Computer Simulation of the SWI Protocol in Nuclear Magnetic Resonance Imaging,
ICCVG14(294-301).
Springer DOI 1410
BibRef

Uma, K., Chandrasekharan, K., Paul, J.S.,
Partial Fourier reconstruction using subspace projection,
NCVPRIPG13(1-4)
IEEE DOI 1408
biomedical MRI BibRef

Hyder, M.M.[Md Mashud], Mahata, K.[Kaushik],
Sparse sequence recovery via a maximum a posteriori estimation,
ICIP13(489-493)
IEEE DOI 1402
Compressed sensing BibRef

Rodrigues, M.L.[Miguel L.], Figueiredo, P.[Patrícia], Sanches, J.M.[João Miguel],
A Bayesian Approach to Perfusion Imaging Using ASL MRI,
IbPRIA13(691-698).
Springer DOI 1307
BibRef

Garcia-Gomez, J.M.[Juan M.], Robles, M.[Montserrat], van Huffel, S.[Sabine], Juan-Císcar, A.[Alfons],
Modelling of Magnetic Resonance Spectra Using Mixtures for Binned and Truncated Data,
IbPRIA07(II: 266-273).
Springer DOI 0706
BibRef

Garnaoui, H.H., Tewfik, A.H.,
Visual masking and the design of magnetic resonance image acquisition,
ICIP95(I: 625-628).
IEEE DOI 9510
BibRef

Kadah, Y.M., Tewfik, A.H.,
Theory of true-velocity duplex imaging using a single transducer,
ICIP95(I: 629-632).
IEEE DOI 9510
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Magnetic Particle Imaging .

Last update:Jan 17, 2021 at 16:22:28