An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

[1]  R. Courant,et al.  On the Partial Difference Equations, of Mathematical Physics , 2015 .

[2]  D. Rueckert,et al.  Automated fetal brain segmentation from 2D MRI slices for motion correction , 2014, NeuroImage.

[3]  Xavier Bresson,et al.  Efficient Total Variation Algorithm for Fetal Brain MRI Reconstruction , 2014, MICCAI.

[4]  Colin Studholme,et al.  Automatic Template-based Brain Extraction in Fetal MR Images , 2013 .

[5]  J V Hajnal,et al.  Motion-Compensation Techniques in Neonatal and Fetal MR Imaging , 2013, American Journal of Neuroradiology.

[6]  Mary A. Rutherford,et al.  Reconstruction of fetal brain MRI with intensity matching and complete outlier removal , 2012, Medical Image Anal..

[7]  Wiro J Niessen,et al.  Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time? , 2012, Magnetic resonance in medicine.

[8]  Eric Van Reeth,et al.  Super-resolution in magnetic resonance imaging: A review , 2012 .

[9]  Jeffrey A. Fessler,et al.  Regularization Parameter Selection for Nonlinear Iterative Image Restoration and MRI Reconstruction Using GCV and SURE-Based Methods , 2012, IEEE Transactions on Image Processing.

[10]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[11]  Simon K. Warfield,et al.  Fetal brain volumetry through MRI volumetric reconstruction and segmentation , 2011, International Journal of Computer Assisted Radiology and Surgery.

[12]  D. Louis Collins,et al.  Non-local MRI upsampling , 2010, Medical Image Anal..

[13]  C. Studholme,et al.  3D global and regional patterns of human fetal subplate growth determined in utero , 2010, Brain Structure and Function.

[14]  N. Zabaras,et al.  Solving Stochastic Inverse Problems: A Sparse Grid Collocation Approach , 2010 .

[15]  Colin Studholme,et al.  On Super-Resolution for Fetal Brain MRI , 2010, MICCAI.

[16]  F. Rousseau.  A non-local approach for image super-resolution using intermodality priors , 2010, Medical Image Anal..

[17]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.

[18]  Patrick L. Combettes,et al.  Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.

[19]  Isabelle Bloch,et al.  Automatic segmentation of head structures on fetal MRI , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[20]  Xie Kai,et al.  Arnoldi process based on optimal estimation of the regularization parameter , 2009, 2009 IEEE International Workshop on Imaging Systems and Techniques.

[21]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[22]  Colin Studholme,et al.  Intersection based registration of slice stacks to form 3D images of the human fetal brain , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  Daniel Rueckert,et al.  MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies , 2007, IEEE Transactions on Medical Imaging.

[24]  Colin Studholme,et al.  Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. , 2006, Academic radiology.

[25]  C. Garel.  New advances in fetal MR neuroimaging , 2006, Pediatric Radiology.

[26]  Daniela Prayer,et al.  Methods of fetal MR: beyond T2-weighted imaging , 2006 .

[27]  Yurii Nesterov,et al.  Smooth minimization of non-smooth functions , 2005, Math. Program..

[28]  Michel Barlaud,et al.  Deterministic edge-preserving regularization in computed imaging , 1997, IEEE Trans. Image Process..

[29]  R R Edelman,et al.  Fetal anatomy revealed with fast MR sequences. , 1996, AJR. American journal of roentgenology.

[30]  Dianne P. O'Leary,et al.  The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems , 1993, SIAM J. Sci. Comput..

[31]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[32]  Nikolas P. Galatsanos,et al.  Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation , 1992, IEEE Trans. Image Process..

[33]  R. Mersereau,et al.  Optimal estimation of the regularization parameter and stabilizing functional for regularized image restoration , 1990 .

[34]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[35]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[36]  C. Stein.  Estimation of the Mean of a Multivariate Normal Distribution , 1981 .

[37]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[38]  Stanley Win Kler,et al.  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2015 .

[39]  J. Thiran,et al.  Automated Brain Extraction in Fetal MRI by Multi-Atlas Fusion Strategy: Study on Healthy and Pathological Subjects , 2014 .

[40]  Colin Studholme,et al.  BTK: An open-source toolkit for fetal brain MR image processing , 2013, Comput. Methods Programs Biomed..

[41]  Colin Studholme,et al.  A unified approach for motion estimation and super resolution reconstruction from structural Magnetic Resonance imaging on moving subjects , 2013 .

[42]  G. Langs,et al.  Fully Automated Brain Extraction and Orientation in Raw Fetal MRI , 2013 .

[43]  Julia A. Scott,et al.  Early folding patterns and asymmetries of the normal human brain detected from in utero MRI. , 2012, Cerebral cortex.

[44]  Colin Studholme,et al.  Intersection Based Motion Correction of Multislice MRI for 3-D in Utero Fetal Brain Image Formation , 2010, IEEE Transactions on Medical Imaging.

[45]  Simon K. Warfield,et al.  Super-resolution Reconstruction of Fetal Brain MRI , 2009 .

[46]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[47]  Mingqiang Zhu,et al.  An Efficient Primal-Dual Hybrid Gradient Algorithm For Total Variation Image Restoration , 2008 .

[48]  W. Clem Karl,et al.  3.6 – Regularization in Image Restoration and Reconstruction , 2005 .

[49]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[50]  Orit A Glenn,et al.  Fetal MRI: a developing technique for the developing patient. , 2004, AJR. American journal of roentgenology.

[51]  Harry Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.

[53]  Stanley J. Reeves,et al.  Perceptual evaluation of the mean-square error choice of regularization parameter , 1995, IEEE Trans. Image Process..

[54]  R. Glowinski,et al.  Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics , 1987 .