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Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.

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Τίτλος: Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.
Συγγραφείς: Blumenthal M; Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria., Uecker M; Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria.; Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.; DZHK (German Centre for Cardiovascular Research), Germany.; BioTechMed-Graz, Graz, Austria.
Πηγή: Magnetic resonance in medicine [Magn Reson Med] 2026 Jul; Vol. 96 (1), pp. 134-145. Date of Electronic Publication: 2026 Mar 12.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE
Imprint Name(s): Publication: 1999- : New York, NY : Wiley
Original Publication: San Diego : Academic Press
Ιατρικοί όροι (MeSH): Image Processing, Computer-Assisted*/methods , Magnetic Resonance Imaging*/methods , Heart*/diagnostic imaging, Brain/diagnostic imaging ; Image Enhancement/methods ; Humans ; Algorithms ; Nonlinear Dynamics ; Reproducibility of Results ; Sensitivity and Specificity
Περίληψη: Purpose: Phase singularities are a common problem in image reconstruction with auto-calibrated sensitivities due to an inherent ambiguity of the estimation problem. The purpose of this work is to develop a method for detecting and correcting phase poles in non-linear inverse (NLINV) reconstruction of MR images and coil sensitivity maps.
Methods: Phase poles are detected in individual coil sensitivity maps by computing the curl in each pixel. A weighted average of the curl in each coil is computed to detect phase poles. Phase pole detection and correction is then integrated into the iteratively regularized Gauss-Newton method of the NLINV algorithm. In addition, we demonstrate the algorithm can also remove phase poles in ESPIRiT coil sensitivity maps. Phase pole correction is evaluated for reconstruction of accelerated Cartesian MPRAGE data of the brain and interactive radial real-time MRI of the human heart.
Results: For both applications, phase pole correction can be used for estimation of coil sensitivity profiles free from singularities. For NLINV, we demonstrate reliable and efficient estimation even from very small ( INLINEMATH ) auto-calibration (AC) regions.
Conclusion: With the proposed method, phase poles can be reliably removed in reconstructions and coil sensitivities.
(© 2026 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
Σχόλια: Update of: ArXiv. 2025 Aug 6:arXiv:2508.04685v1.. (PMID: 40799814)
References: P. B. Roemer, W. A. Edelstein, C. E. Hayes, S. P. Souza, and O. M. Mueller, “The NMR Phased Array,” Magnetic Resonance in Medicine 16 (1990): 192–225.
D. O. Walsh, A. F. Gmitro, and M. W. Marcellin, “Adaptive Reconstruction of Phased Array MR Imagery,” Magnetic Resonance in Medicine 43 (2000): 682–690.
L. Ying and J. Sheng, “Joint Image Reconstruction and Sensitivity Estimation in SENSE (JSENSE),” Magnetic Resonance in Medicine 57 (2007): 1196–1202.
M. Uecker, T. Hohage, K. T. Block, and J. Frahm, “Image Reconstruction by Regularized Nonlinear Inversion‐Joint Estimation of Coil Sensitivities and Image Content,” Magnetic Resonance in Medicine 60 (2008): 674–682.
M. Uecker, P. Lai, M. J. Murphy, et al., “ESPIRiT—An Eigenvalue Approach to Autocali‐Brating Parallel MRI: Where SENSE Meets GRAPPA,” Magnetic Resonance in Medicine 71 (2014): 990–1001.
R. A. Lobos, C. C. Chan, and J. P. Haldar, “New Theory and Faster Computations for Subspace‐Based Sensitivity Map Estimation in Multichannel MRI,” IEEE Transactions on Medical Imaging 43 (2024): 286–296.
M. Griswold, D. Walsh, R. Heidemann, A. Haase, and P. Jakob, “The Use of an Adaptive Reconstruction for Array Coil Sensitivity Mapping and Intensity Normalization,” in Proceedings of the Annual Meeting ISMRM & SMRT, vol. 10 (2002), 2410.
M. Buehrer, K. P. Pruessmann, P. Boesiger, and S. Kozerke, “Array Compression for MRI With Large Coil Arrays,” Magnetic Resonance in Medicine 57 (2007): 1131–1139.
M. Buehrer, P. Boesiger, and S. Kozerke, “Virtual Body Coil Calibration for Phased‐Array Imaging,” in Proceedings of the Annual Meeting ISMRM & SMRT, vol. 17 (2009), 760.
D. L. Parker, A. Payne, N. Todd, and J. R. Hadley, “Phase Reconstruction From Multiple Coil Data Using a Virtual Reference Coil,” Magnetic Resonance in Medicine 72 (2014): 563–569.
B. Bilgic, J. P. Marques, L. L. Wald, and K. Setsompop, “Block Coil Compression for Virtual Body Coil Without Phase Singularities,” in Proceedings of the Fourth International Workshop on MRI Phase Contrast & Quantitative Susceptibility Mapping (2016).
S. J. Inati, M. S. Hansen, and P. Kellman, “A Solution to the Phase Problem in Adaptive Coil Combination,” in Proceedings of the Annual Meeting ISMRM & SMRT (2013), 2672.
M. Uecker and M. Lustig, “Estimating Absolute‐Phase Maps Using ESPIRiT and Virtual Conjugate Coils,” Magnetic Resonance in Medicine 77 (2017): 1201–1207.
S. Chavez, Q. S. Xiang, and L. An, “Understanding Phase Maps in MRI: A New Cutline Phase Unwrapping Method,” IEEE Transactions on Medical Imaging 21 (2002): 966–977.
S. D. Robinson, K. Bredies, D. Khabipova, B. Dymerska, J. P. Marques, and F. Schweser, “An Illustrated Comparison of Processing Methods for MR Phase Imaging and QSM: Combining Array Coil Signals and Phase Unwrapping,” NMR in Biomedicine 30 (2017): e3601.
Q. C. O. Committee, B. Bilgic, M. Costagli, et al., “Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in the Brain: A Consensus of the ISMRM Electro‐Magnetic Tissue Properties Study Group,” Magnetic Resonance in Medicine 91 (2024): 1834–1862.
N. Li, W. T. Wang, D. L. Pham, and J. A. Butman, “Artifactual Microhemorrhage Generated by Susceptibility Weighted Image Processing,” Journal of Magnetic Resonance Imaging 41 (2015): 1695–1700.
K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, “SENSE: Sensitivity Encoding for Fast MRI,” Magnetic Resonance in Medicine 42 (1999): 952–962.
K. P. Pruessmann, M. Weiger, P. Boernert, and P. Boesiger, “Advances in Sensitivity Encoding With Arbitrary K‐Space Trajectories,” Magnetic Resonance in Medicine 46 (2001): 638–651.
K. T. Block, M. Uecker, and J. Frahm, “Undersampled Radial MRI With Multiple Coils. Iterative Image Reconstruction Using a Total Variation Constraint,” Magnetic Resonance in Medicine 57 (2007): 1086–1098.
M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging,” Magnetic Resonance in Medicine 58 (2007): 1182–1195.
Z. Feng, D. C. Noll, J. F. Nielsen, and J. A. Fessler, “Separate Magnitude and Phase Regularization via Compressed Sensing,” IEEE Transactions on Medical Imaging 31 (2012): 1713–1723.
F. Ong, J. Y. Cheng, and M. Lustig, “General Phase Regularized Reconstruction Using Phase Cycling,” Magnetic Resonance in Medicine 80 (2017): 112–125.
Y. Liu and J. P. Haldar, “PALMNUT: An Enhanced Proximal Alternating Linearized Minimization Algorithm With Application to Separate Regularization of Magnitude and Phase,” IEEE Transactions on Computational Imaging 7 (2021): 518–530.
M. A. Griswold, P. M. Jakob, R. M. Heidemann, et al., “Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA),” Magnetic Resonance in Medicine 47 (2002): 1202–1210.
M. Lustig and J. M. Pauly, “SPIRiT: Iterative Self‐Consistent Parallel Imaging Reconstruction From Arbitrary k‐Space,” Magnetic Resonance in Medicine 64 (2010): 457–471.
J. P. Haldar, “Low‐Rank Modeling of Local k‐Space Neighborhoods (LORAKS) for Constrained MRI,” IEEE Transactions on Medical Imaging 33 (2014): 668–681.
P. J. Shin, P. E. Z. Larson, M. A. Ohliger, et al., “Calibrationless Parallel Imaging Reconstruction Based on Structured Low‐Rank Matrix Completion,” Magnetic Resonance in Medicine 72 (2014): 959–970.
X. Wang, Z. Tan, N. Scholand, V. Roeloffs, and M. Uecker, “Physics‐Based Reconstruction Methods for Magnetic Resonance Imaging,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379 (2021): 20200196.
M. Uecker, S. Zhang, and J. Frahm, “Nonlinear Inverse Reconstruction for Real‐Time MRI of the Human Heart Using Undersampled Radial FLASH,” Magnetic Resonance in Medicine 63 (2010): 1456–1462.
H. C. M. Holme and M. Uecker, “Non‐Linear Reconstruction for Coil Sensitivity Calibration From Cartesian and Non‐Cartesian Data,” in Proceedings of the Annual Meeting ISMRM & SMRT (2023), 4616.
O. Maier, J. Schoormans, M. Schloegl, et al., “Rapid T1 Quantification From High Resolution 3D Data With Model‐Based Reconstruction,” Magnetic Resonance in Medicine 81 (2019): 2072–2089.
W. Zeng, J. Peng, S. Wang, and Q. Liu, “A Comparative Study of CNN‐Based Super‐Resolution Methods in MRI Reconstruction and Its Beyond,” Signal Processing: Image Communication 81 (2020): 115701.
N. Dubljevic, S. Moore, M. L. Lauzon, R. Souza, and R. Frayne, “Effect of MR Head Coil Geometry on Deep‐Learning‐Based MR Image Reconstruction,” Magnetic Resonance in Medicine 92 (2024): 1404–1420.
N. G. Lee, G. Bauman, O. Bieri, and K. S. Nayak, “Replication of the bSTAR Sequence and Open‐Source Implementation,” Magnetic Resonance in Medicine 91 (2024): 1464–1477.
B. Tasdelen, N. G. Lee, S. X. Cui, and K. S. Nayak, “Improved Abdominal T1 Weighted Imaging at 0.55T,” Magnetic Resonance in Medicine 92 (2024): 2580–2587.
F. Klimeš, J. W. Plummer, M. M. Willmering, et al., “Quantifying Spatial and Dynamic Lung Abnormalities With 3D PREFUL FLORET UTE Imaging: A Feasibility Study,” Magnetic Resonance in Medicine 93 (2025): 1984–1998.
S. Feizollah and C. L. Tardif, “3D MERMAID: 3D Multi‐Shot Enhanced Recovery Motion Artifact Insensitive Diffusion for Submillimeter, Multi‐Shell, and SNR‐Efficient Diffusion Imaging,” Magnetic Resonance in Medicine 93 (2025): 2311–2330.
B. T. D. Vu, N. Kamona, Y. Kim, et al., “Three Contrasts in 3 Min: Rapid, High‐Resolution, and Bone‐Selective UTE MRI for Craniofacial Imaging With Automated Deep‐Learning Skull Segmentation,” Magnetic Resonance in Medicine 93 (2025): 245–260.
B. T. D. Vu, N. Kamona, F. W. Wehrli, et al., “DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue,” Magnetic Resonance in Medicine 95 (2026): 1513–1527.
F. Knoll, C. Clason, K. Bredies, M. Uecker, and R. Stollberger, “Parallel Imaging With Nonlinear Reconstruction Using Variational Penalties,” Magnetic Resonance in Medicine 67 (2012): 34–41.
S. Rosenzweig, H. C. M. Holme, R. N. Wilke, D. Voit, J. Frahm, and M. Uecker, “Simultaneous Multi‐Slice MRI Using Cartesian and Radial FLASH and Regularized Nonlinear Inversion: SMS‐NLINV,” Magnetic Resonance in Medicine 79 (2018): 2057–2066.
H. C. M. Holme, S. Rosenzweig, F. Ong, R. N. Wilke, M. Lustig, and M. Uecker, “ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging,” Scientific Reports 9 (2019): 3034.
M. Uecker, S. Zhang, D. Voit, A. Karaus, K. D. Merboldt, and J. Frahm, “Real‐Time MRI at a Resolution of 20 Ms,” NMR in Biomedicine 23 (2010): 986–994.
A. P. Leynes, N. Deveshwar, S. S. Nagarajan, and P. E. Z. Larson, “Scan‐Specific Self‐Supervised Bayesian Deep Non‐Linear Inversion for Undersampled MRI Reconstruction,” IEEE Transactions on Medical Imaging 43 (2024): 2358–2369.
M. Blumenthal, C. Fantinato, C. Unterberg‐Buchwald, M. Haltmeier, X. Wang, and M. Uecker, “Self‐Supervised Learning for Improved Calibrationless Radial MRI With NLINV‐Net,” Magnetic Resonance in Medicine 92 (2024): 2447–2463.
A. A. Joseph, K. D. Merboldt, D. Voit, et al., “Real‐Time Phase‐Contrast MRI of Cardiovascular Blood Flow Using Undersampled Radial Fast Low‐Angle Shot and Nonlinear Inverse Reconstruction,” NMR in Biomedicine 25 (2011): 917–924.
A. Niebergall, S. Zhang, E. Kunay, et al., “Real‐Time MRI of Speaking at a Resolution of 33 Ms: Undersampled Radial FLASH With Nonlinear Inverse Reconstruction,” Magnetic Resonance in Medicine 69 (2013): 477–485.
B. Xu, P. Spincemaille, G. Chen, et al., “Fast 3D Contrast Enhanced MRI of the Liver Using Temporal Resolution Acceleration With Constrained Evolution Reconstruction,” Magnetic Resonance in Medicine 69 (2013): 370–381.
A. Joseph, J. T. Kowallick, K. D. Merboldt, et al., “Real‐Time Flow MRI of the Aorta at a Resolution of 40 Msec,” Journal of Magnetic Resonance Imaging 40 (2014): 206–213.
M. Untenberger, Z. Tan, D. Voit, et al., “Advances in Real‐Time Phase‐Contrast Flow MRI Using Asymmetric Radial Gradient Echoes,” Magnetic Resonance in Medicine 75 (2016): 1901–1908.
C. Wang, F. F. Yin, J. P. Kirkpatrick, and Z. Chang, “Accelerated Brain DCE‐MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study,” Technology in Cancer Research & Treatment 16 (2017): 446–460.
Z. Chen, Y. Ren, S. Su, et al., “Joint Reconstruction of Multi‐Contrast Images and Multi‐Channel Coil Sensitivities,” Applied Magnetic Resonance 48 (2017): 955–969.
Y. Dong, M. Riedel, K. Koolstra, M. J. P. van Osch, and P. Börnert, “Water/Fat Separation for Self‐Navigated Diffusion‐Weighted Multishot Echo‐Planar Imaging,” NMR in Biomedicine 36 (2023): e4822.
J. Xiang, J. Lamy, R. Lampert, and D. C. Peters, “Balanced Steady‐State Free Precession Cine MR Imaging in the Presence of Cardiac Devices: Value of Interleaved Radial Linear Combination Acquisition With Partial Dephasing,” Journal of Magnetic Resonance Imaging 58 (2023): 782–791.
X. Wang, H. Fan, Z. Tan, et al., “Rapid, High‐Resolution and Distortion‐Free R2* Mapping of Fetal Brain Using Multi‐Echo Radial FLASH and Model‐Based Reconstruction,” Magnetic Resonance in Medicine 94 (2025): 1913–1929.
N. M. Brisson, M. Krämer, L. A. N. Krahl, A. Schill, G. N. Duda, and J. R. Reichenbach, “A Novel Multipurpose Device for Guided Knee Motion and Loading During Dynamic Magnetic Resonance Imaging,” Zeitschrift für Medizinische Physik 32 (2022): 500–513.
S. Zhang, A. Olthoff, and J. Frahm, “Real‐Time Magnetic Resonance Imaging of Normal Swallowing,” Journal of Magnetic Resonance Imaging 35 (2012): 1372–1379.
E. C. Bassett, E. G. Kholmovski, B. D. Wilson, et al., “Evaluation of Highly Accelerated Real‐Time Cardiac Cine MRI in Tachycardia,” NMR in Biomedicine 27 (2014): 175–182.
A. Olthoff, S. Zhang, R. Schweizer, and J. Frahm, “On the Physiology of Normal Swallowing as Revealed by Magnetic Resonance Imaging in Real Time,” Gastroenterology Research and Practice 2014 (2014): 10.
P. O. Carstens, S. Zhang, A. Olthoff, et al., “Real‐Time MRI for Evaluation of Dysphagia in Inclusion Body Myositis (IBM) (P2. 015),” Neurology 84 (2015): P2.015.
X. Liu, P. Karmarkar, D. Voit, et al., “Real‐Time High‐Resolution MRI Endoscopy at up to 10 Frames Per Second,” BME Frontiers 2021 (2021): 6185616.
K. Isaieva, Y. Laprie, J. Lecl‘ere, I. K. Douros, J. Felblinger, and P. A. Vuissoz, “Multimodal Dataset of Real‐Time 2D and Static 3D MRI of Healthy French Speakers,” Sci Data 8 (2021): 258.
K. Laubrock, T. von Loesch, M. Steinmetz, et al., “Imaging of Arrhythmia: Real‐Time Cardiac Magnetic Resonance Imaging in Atrial Fibrillation,” European Journal of Radiology Open 9 (2022): 100404.
X. Wang, V. Roeloffs, J. Klosowski, et al., “Model‐Based T1 Mapping With Sparsity Constraints Using Single‐Shot Inversion‐Recovery Radial FLASH,” Magnetic Resonance in Medicine 79 (2018): 730–740.
D. Voit, O. Kalentev, and J. Frahm, “Body Coil Reference for Inverse Reconstructions of Multi‐Coil Data—The Case for Real‐Time MRI,” Quantitative Imaging in Medicine and Surgery 9 (2019): 1815–1819.
M. Berry, “Singularities in Waves,” in Les Houches Lecture Series Session XXXV (North‐Holland, 1981), 453–543.
D. I. Hoult and R. E. Richards, “The Signal‐To‐Noise Ratio of the Nuclear Magnetic Resonance Experiment,” Journal of Magnetic Resonance 24 (1976): 71–85.
T. Zhang, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “Coil Compression for Accelerated Imaging With Cartesian Sampling,” Magnetic Resonance in Medicine 69 (2013): 571–582.
D. Kim, S. F. Cauley, K. S. Nayak, R. M. Leahy, and J. P. Haldar, “Region‐Optimized Virtual (ROVir) Coils: Localization and/or Suppression of Spatial Regions Using Sensor‐Domain Beam‐Forming,” Magnetic Resonance in Medicine 86 (2021): 197–212.
M. Uecker, P. Virtue, F. Ong, et al., “Software Toolbox and Programming Library for Compressed Sensing and Parallel Imaging,” in ISMRM Workshop on Data Sampling and Image Reconstruction (2013).
M. Uecker, F. Ong, J. I. Tamir, et al., “Berkeley Advanced Reconstruction Toolbox,” in Proceedings of the Annual Meeting ISMRM & SMRT, vol. 23 (2015), 2486.
S. Rosenzweig, H. C. M. Holme, and M. Uecker, “Simple Auto‐Calibrated Gradient Delay Estimation From Few Spokes Using Radial Intersections (RING),” Magnetic Resonance in Medicine 81 (2019): 1898–1906.
P. Schaten, M. Blumenthal, B. Rapp, and M. Uecker, “BART Streams: A Plug & Play Framework for Interactive Real‐Time MRI at the Example of Aligned Dynamic Coil Compression,” Magnetic Resonance Materials in Physics, Biology and Medicine 37 (2024): 719–721.
S. Schaetz, D. Voit, J. Frahm, and M. Uecker, “Accelerated Computing in Magnetic Resonance Imaging: Real‐Time Imaging Using Nonlinear Inverse Reconstruction,” Computational and Mathematical Methods in Medicine 2017 (2017): 1–11.
Grant Information: U24EB029240 United States NH NIH HHS; 432680300-SFB 1456 Deutsche Forschungsgemeinschaft; 81Z0300115 Deutsches Zentrum für Herz-Kreislaufforschung; 10.55776/F100800 Austrian Science Fund
Contributed Indexing: Keywords: MRI; image reconstruction; non‐linear inverse problems; parallel imaging; phase singularity
Entry Date(s): Date Created: 20260312 Date Completed: 20260710 Latest Revision: 20260710
Update Code: 20260711
PubMed Central ID: PMC13156459
DOI: 10.1002/mrm.70333
PMID: 41820227
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1522-2594
DOI:10.1002/mrm.70333