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1509
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biomedical MRI
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approximation theory
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1601
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magnetic resonance imaging
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1608
BibRef
Eksioglu, E.M.[Ender M.],
Decoupled Algorithm for MRI Reconstruction Using Nonlocal Block
Matching Model: BM3D-MRI,
JMIV(56), No. 3, November 2016, pp. 430-440.
WWW Link.
1609
BibRef
Wu, Y.C.[Ye-Cun],
Du, H.[Huiqian],
Mei, W.[Wenbo],
Filter-based compressed sensing MRI reconstruction,
IJIST(26), No. 3, 2016, pp. 173-178.
DOI Link
1609
compressed sensing (CS)
BibRef
Bahrami, K.,
Shi, F.,
Zong, X.,
Shin, H.W.,
An, H.,
Shen, D.,
Reconstruction of 7T-Like Images From 3T MRI,
MedImg(35), No. 9, September 2016, pp. 2085-2097.
IEEE DOI
1609
Correlation
BibRef
Franke, J.,
Heinen, U.,
Lehr, H.,
Weber, A.,
Jaspard, F.,
Ruhm, W.,
Heidenreich, M.,
Schulz, V.,
System Characterization of a Highly Integrated Preclinical Hybrid
MPI-MRI Scanner,
MedImg(35), No. 9, September 2016, pp. 1993-2004.
IEEE DOI
1609
Biomedical imaging
BibRef
Liu, Y.,
Zhan, Z.,
Cai, J.F.,
Guo, D.,
Chen, Z.,
Qu, X.,
Projected Iterative Soft-Thresholding Algorithm for Tight Frames in
Compressed Sensing Magnetic Resonance Imaging,
MedImg(35), No. 9, September 2016, pp. 2130-2140.
IEEE DOI
1609
Algorithm design and analysis
BibRef
Ehrhardt, M.J.[Matthias J.],
Betcke, M.M.[Marta M.],
Multicontrast MRI Reconstruction with Structure-Guided Total
Variation,
SIIMS(9), No. 3, 2016, pp. 1084-1106.
DOI Link
1610
BibRef
Natterer, F.[Frank],
Image Reconstruction in Quantitative Susceptibility Mapping,
SIIMS(9), No. 3, 2016, pp. 1127-1131.
DOI Link
1610
MRI data.
BibRef
Roohi, S.F.[Shahrooz F.],
Zonoobi, D.[Dornoosh],
Kassim, A.A.[Ashraf A.],
Jaremko, J.L.[Jacob L.],
Multi-dimensional low rank plus sparse decomposition for
reconstruction of under-sampled dynamic MRI,
PR(63), No. 1, 2017, pp. 667-679.
Elsevier DOI
1612
BibRef
Earlier:
Dynamic MRI reconstruction using low rank plus sparse tensor
decomposition,
ICIP16(1769-1773)
IEEE DOI
1610
Low-rank and sparse tensor decomposition.
Image reconstruction
BibRef
Zonoobi, D.[Dornoosh],
Roohi, S.F.[Shahrooz F.],
Kassim, A.A.[Ashraf A.],
Jaremko, J.L.[Jacob L.],
Dependent nonparametric bayesian group dictionary learning for online
reconstruction of dynamic MR images,
PR(63), No. 1, 2017, pp. 518-530.
Elsevier DOI
1612
Dynamic 3D MRI
BibRef
Lu, J.Y.,
Grafendorfer, T.,
Zhang, T.,
Vasanawala, S.,
Robb, F.,
Pauly, J.M.,
Scott, G.C.,
Depletion-Mode GaN HEMT Q-Spoil Switches for MRI Coils,
MedImg(35), No. 12, December 2016, pp. 2558-2567.
IEEE DOI
1612
Coils
BibRef
Mehta, J.[Janki],
Majumdar, A.[Angshul],
RODEO: Robust DE-aliasing autoencOder for real-time medical image
reconstruction,
PR(63), No. 1, 2017, pp. 499-510.
Elsevier DOI
1612
Autoencoder
BibRef
Liu, F.,
Velikina, J.V.,
Block, W.F.,
Kijowski, R.,
Samsonov, A.A.,
Fast Realistic MRI Simulations Based on Generalized Multi-Pool
Exchange Tissue Model,
MedImg(36), No. 2, February 2017, pp. 527-537.
IEEE DOI
1702
Biological system modeling
BibRef
Hennersperger, C.,
Fuerst, B.,
Virga, S.,
Zettinig, O.,
Frisch, B.,
Neff, T.,
Navab, N.,
Towards MRI-Based Autonomous Robotic US Acquisitions:
A First Feasibility Study,
MedImg(36), No. 2, February 2017, pp. 538-548.
IEEE DOI
1702
Imaging
BibRef
Aggarwal, K.[Kamal],
Joshi, K.R.[Kiran R.],
Rajavi, Y.[Yashar],
Taghivand, M.[Mazhareddin],
Pauly, J.M.[John M.],
Poon, A.S.Y.[Ada S.Y.],
Scott, G.[Greig],
A Millimeter-Wave Digital Link for Wireless MRI,
MedImg(36), No. 2, February 2017, pp. 574-583.
IEEE DOI
1702
Short range, high bandwidth transmission.
Baseband
BibRef
Arevalillo-Herráez, M.,
Cobos, M.,
García-Pineda, M.,
A Robust Wrap Reduction Algorithm for Fringe Projection Profilometry
and Applications in Magnetic Resonance Imaging,
IP(26), No. 3, March 2017, pp. 1452-1465.
IEEE DOI
1703
discrete Fourier transforms
BibRef
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.,
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Liu, X.,
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2012
Acceleration, Magnetic resonance imaging, Trajectory,
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2012
Training, Image reconstruction, Magnetic resonance imaging,
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Approximation algorithms, Magnetic resonance imaging,
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2008
Image reconstruction, Coils, Magnetic resonance imaging,
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DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI
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2008
Magnetic resonance imaging, Image restoration,
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2008
Image reconstruction, Generators, Training,
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2004
Image reconstruction, Magnetic resonance imaging, Sensitivity,
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Fast Super-Resolution in MRI Images Using Phase Stretch Transform,
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ICIP19(2876-2880)
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1910
Medical image enhancement, Zero-data learning,
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ICIP19(2080-2084)
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1910
MRI reconstruction, total variation,
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Gradient Coils Design with Regularization Method for Superconducting
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IVCNZ18(1-5)
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1902
Coils, Magnetic resonance imaging, Magnetic noise,
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Kaur, P.,
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MR-Srnet: Transformation of Low Field MR Images to High Field MR
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1809
Image reconstruction, Decoding,
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gradient methods, image restoration,
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DICTA17(1-8)
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Fourier analysis, biomedical MRI, compressed sensing,
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Sun, L.,
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Compressed sensing MRI using total variation regularization with
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ICIP17(3061-3065)
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1803
Frequency measurement, Image reconstruction, Linear programming,
Magnetic resonance imaging, Optimization, TV,
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Bayesian myopic parallel MRI reconstruction,
ISIVC16(103-108)
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1704
Bayes methods
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1703
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MR imaging near metal: The POP algorithm,
ICVNZ16(1-6)
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Fats
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Coherence Analysis of Compressive Sensing Based Magnetic Resonance
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DICTA16(1-7)
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Coherence
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Discrete Fourier transforms
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Jin, K.H.,
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Acceleration
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Zhao, Q.,
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A Novel Sparsity Measure for Tensor Recovery,
ICCV15(271-279)
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Brain modeling
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ICIP15(1026-1030)
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ICIP15(4823-4827)
IEEE DOI
1512
Anisotropic Diffusion; Image Sequence Filtering; MRI; Regularization
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Omer, O.A.[Osama A.],
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Algorithm design and analysis
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biomedical MRI
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Magnetic Particle Imaging .