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

Hue, Y.K., Guimaraes, A.R., Cohen, O., Nevo, E., Roth, A., Ackerman, J.L.,
Magnetic Resonance Mediated Radiofrequency Ablation,
MedImg(37), No. 2, February 2018, pp. 417-427.
IEEE DOI 1802
Heating systems, Magnetic resonance, Magnetic resonance imaging, Power transmission lines, Radio frequency, Tumors, Wires, MRI, radiofrequency ablation (RFA) 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

Muslu, Y., Utkur, M., Demirel, O.B., Saritas, E.U.,
Calibration-Free Relaxation-Based Multi-Color Magnetic Particle Imaging,
MedImg(37), No. 8, August 2018, pp. 1920-1931.
IEEE DOI 1808
Nanoparticles, Imaging, Mirrors, Estimation, Adiabatic, Image reconstruction, Calibration, Magnetic particle imaging, mirror symmetry 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

Song, P., Weizman, L., Mota, J.F.C., Eldar, Y.C., Rodrigues, M.R.D.,
Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction,
ICIP18(2880-2884)
IEEE DOI 1809
Magnetic resonance imaging, Dictionaries, Image reconstruction, Machine learning, Noise reduction, Matching pursuit algorithms, guidance information 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.,
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
Segmentation, Features, Models from Magnetic Resonance Data, MRI .

Last update:Jan 25, 2019 at 17:55:53