Spin and Gradient Multiple Overlapping-Echo Detachment Imaging (SAGE-MOLED): Highly Efficient T2, ... , and M0 Mapping for Simultaneous Perfusion and Permeability Measurements.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Spin and Gradient Multiple Overlapping-Echo Detachment Imaging (SAGE-MOLED): Highly Efficient T2, ... , and M0 Mapping for Simultaneous Perfusion and Permeability Measurements.
Συγγραφείς: Yang Q; Department of Electronic Science, Xiamen University, Xiamen, China.; Department of Radiological Sciences, University of California Irvine, Irvine, California, USA., Bao J; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China., Wang L; Department of Electronic Science, Xiamen University, Xiamen, China., Ge N; Department of Electronic Science, Xiamen University, Xiamen, China., Ma X; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China., Cai S; Department of Electronic Science, Xiamen University, Xiamen, China., Chen Z; Department of Electronic Science, Xiamen University, Xiamen, China., Zhang Y; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China., Gholipour A; Department of Radiological Sciences, University of California Irvine, Irvine, California, USA., Cai C; Department of Electronic Science, Xiamen University, Xiamen, China.
Πηγή: Magnetic resonance in medicine [Magn Reson Med] 2026 Apr; Vol. 95 (4), pp. 1959-1971. Date of Electronic Publication: 2025 Nov 02.
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 , Brain*/diagnostic imaging , Echo-Planar Imaging*/methods , Magnetic Resonance Imaging*, Humans ; Algorithms ; Phantoms, Imaging ; Signal-To-Noise Ratio ; Adult ; Male ; Female ; Permeability ; Computer Simulation ; Cerebrovascular Circulation
Περίληψη: Purpose: Combined spin- and gradient-echo EPI (SAGE-EPI) offers advantages in tissue quantification and dynamic imaging but suffers from low spatial resolution and geometric distortions. This study aims to develop a multiple overlapping-echo detachment-based SAGE acquisition (SAGE-MOLED) to enable efficient, distortion-corrected T2, INLINEMATH , and M0 mapping for perfusion MRI.
Methods: SAGE-MOLED was designed as an optimized MOLED variant by refining echo time sampling and integrating multi-train blip-reversed EPI to enhance distortion correction and temporal SNR, enabling reliable extraction of subject-specific arterial input functions (AIFs). To support dynamic imaging, a steady-state Bloch simulation-based synthetic data framework was developed to simultaneously model T1, T2, and INLINEMATH -related effects, providing training data for an end-to-end deep learning model that enables efficient multiparametric quantification. In addition, a signal-to-concentration model tailored for dynamic MOLED signals was formulated for accurate estimation of permeability and leakage-corrected perfusion parameters. The proposed technique was validated in water phantom experiments, healthy volunteers, and a pilot clinical study.
Results: Single-shot SAGE-MOLED demonstrated high consistency with standard methods in both phantom and in vivo experiments, with Pearson correlation coefficient = 0.991 for T2 and 0.988 for INLINEMATH mapping in the brain. Compared to conventional SAGE-EPI, SAGE-MOLED mitigated geometric distortions and intravoxel dephasing-related signal loss. In perfusion MRI, dynamic SAGE-MOLED enabled simultaneous permeability and leakage-corrected perfusion parameter estimation with a single-dose contrast injection.
Conclusion: SAGE-MOLED overcomes key limitations of SAGE-EPI, providing high-fidelity, distortion-corrected T2, INLINEMATH , and M0 maps for simultaneous quantification of permeability and perfusion parameters.
(© 2025 International Society for Magnetic Resonance in Medicine.)
References: P. Mansfield, “Multi‐Planar Image Formation Using NMR Spin Echoes,” Journal of Physics C: Solid State Physics 10, no. 3 (1977): L55–L58.
R. Rzedzian, B. Chapman, P. Mansfield, et al., “Real‐Time Nuclear Magnetic Resonance Clinical Imaging in Paediatrics,” Lancet 2, no. 8362 (1983): 1281–1282.
C. Y. Liao, X. Z. Cao, J. J. Cho, Z. J. Zhang, K. Setsompop, and B. Bilgic, “Highly Efficient MRI Through Multi‐Shot Echo Planar Imaging,” Conference on Wavelets and Sparsity XVIII, San Diego, CA, August 13–15, 2019.
B. Bilgic, I. Chatnuntawech, M. K. Manhard, et al., “Highly Accelerated Multishot Echo Planar Imaging Through Synergistic Machine Learning and Joint Reconstruction,” Magnetic Resonance in Medicine 82, no. 4 (2019): 1343–1358.
A. D. Cohen, B. L. Yang, B. Fernandez, S. Banerjee, and Y. Wang, “Improved Resting State Functional Connectivity Sensitivity and Reproducibility Using a Multiband Multi‐Echo Acquisition,” NeuroImage 225 (2021): 34.
H. Schmiedeskamp, M. Straka, R. D. Newbould, et al., “Combined Spin‐ and Gradient‐Echo Perfusion‐Weighted Imaging,” Magnetic Resonance in Medicine 68, no. 1 (2012): 30–40.
F. Y. X. Wang, Z. J. Dong, T. G. Reese, et al., “Echo Planar Time‐Resolved Imaging (EPTI),” Magnetic Resonance in Medicine 81, no. 6 (2019): 3599–3615.
Z. J. Dong, L. L. Wald, J. R. Polimeni, and F. Y. X. Wang, “Single‐Shot Echo Planar Time‐Resolved Imaging for Multi‐Echo Functional MRI and Distortion‐Free Diffusion Imaging,” Magnetic Resonance in Medicine 93, no. 3 (2025): 993–1013.
Z. J. Zhang, J. Cho, L. Wang, et al., “Blip Up‐Down Acquisition for Spin‐ and Gradient‐Echo Imaging (BUDA‐SAGE) With Self‐Supervised Denoising Enables Efficient T2, T2*, Para‐ and Dia‐Magnetic Susceptibility Mapping,” Magnetic Resonance in Medicine 88, no. 2 (2022): 633–650.
M. K. Manhard, J. Stockmann, C. Y. Liao, et al., “A Multi‐Inversion Multi‐Echo Spin and Gradient Echo Echo Planar Imaging Sequence With Low Image Distortion for Rapid Quantitative Parameter Mapping and Synthetic Image Contrasts,” Magnetic Resonance in Medicine 86, no. 2 (2021): 866–880.
N. Wang, C. Y. Liao, X. Z. Cao, et al., “Spherical Echo‐Planar Time‐Resolved Imaging (sEPTI) for Rapid 3D Quantitative T2* and Susceptibility Imaging,” Magnetic Resonance in Medicine 93, no. 1 (2025): 121–137.
Z. J. Dong, T. G. Reese, H. H. Lee, et al., “Romer‐EPTI: Rotating‐View Motion‐Robust Super‐Resolution EPTI for SNR‐Efficient Distortion‐Free In‐Vivo Mesoscale Diffusion MRI and Microstructure Imaging,” Magnetic Resonance in Medicine 93, no. 4 (2024): 1535–1555.
C. Y. Liao, B. Bilgic, Q. Y. Tian, et al., “Distortion‐Free, High‐Isotropic‐Resolution Diffusion MRI With gSlider BUDA‐EPI and Multicoil Dynamic B0 Shimming,” Magnetic Resonance in Medicine 86, no. 2 (2021): 791–803.
P. Kundu, N. D. Brenowitz, V. Voon, et al., “Integrated Strategy for Improving Functional Connectivity Mapping Using Multiecho fMRI,” Proceedings of the National Academy of Sciences of the United States of America 110, no. 40 (2013): 16187–16192.
E. G. Keeling, M. Bergamino, S. Ragunathan, C. C. Quarles, A. T. Newton, and A. M. Stokes, “Optimization and Validation of Multi‐Echo, Multi‐Contrast SAGE Acquisition in fMRI,” Imaging Neuroscience 2 (2024): 1–20.
H. Schmiedeskamp, J. B. Andre, M. Straka, et al., “Simultaneous Perfusion and Permeability Measurements Using Combined Spin‐ and Gradient‐Echo MRI,” Journal of Cerebral Blood Flow and Metabolism 33, no. 5 (2013): 732–743.
A. M. Stokes, S. Ragunathan, R. K. Robison, et al., “Development of a Spiral Spin‐ and Gradient‐Echo (Spiral‐SAGE) Approach for Improved Multi‐Parametric Dynamic Contrast Neuroimaging,” Magnetic Resonance in Medicine 86, no. 6 (2021): 3082–3095.
C. C. Quarles, L. C. Bell, and A. M. Stokes, “Imaging Vascular and Hemodynamic Features of the Brain Using Dynamic Susceptibility Contrast and Dynamic Contrast Enhanced MRI,” NeuroImage 187 (2019): 32–55.
M. A. Fernández‐Seara and F. W. Wehrli, “Postprocessing Technique to Correct for Background Gradients in Image‐Based R2* Measurements,” Magnetic Resonance in Medicine 44, no. 3 (2000): 358–366.
F. Küppers, S. D. Yun, and N. J. Shah, “Development of a Novel 10‐Echo Multi‐Contrast Sequence Based on EPIK to Deliver Simultaneous Quantification of T2 and T2* With Application to Oxygen Extraction Fraction,” Magnetic Resonance in Medicine 88, no. 4 (2022): 1608–1623.
V. G. Kiselev, R. Strecker, S. Ziyeh, O. Speck, and J. Hennig, “Vessel Size Imaging in Humans,” Magnetic Resonance in Medicine 53, no. 3 (2005): 553–563.
Z. H. Hu, A. G. Christodoulou, N. Wang, et al., “MR Multitasking‐Based Dynamic Imaging for Cerebrovascular Evaluation (MT‐DICE): Simultaneous Quantification of Permeability and Leakage‐Insensitive Perfusion by Dynamic T1/T2* Mapping,” Magnetic Resonance in Medicine 89, no. 1 (2023): 161–176.
J. J. Wu, A. M. Saindane, X. D. Zhong, and D. Q. Qiu, “Simultaneous Perfusion and Permeability Assessments Using Multiband Multi‐Echo EPI (M2‐EPI) in Brain Tumors,” Magnetic Resonance in Medicine 81, no. 3 (2019): 1755–1768.
M. A. Griswold, P. M. Jakob, R. M. Heidemann, et al., “Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA),” Magnetic Resonance in Medicine 47, no. 6 (2002): 1202–1210.
K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, “SENSE: Sensitivity Encoding for Fast MRI,” Magnetic Resonance in Medicine 42, no. 5 (1999): 952–962.
H. Schmiedeskamp, M. Straka, and R. Bammer, “Compensation of Slice Profile Mismatch in Combined Spin‐ and Gradient‐Echo Echo‐Planar Imaging Pulse Sequences,” Magnetic Resonance in Medicine 67, no. 2 (2012): 378–388.
A. M. Stokes, J. T. Skinner, T. Yankeelov, and C. C. Quarles, “Assessment of a Simplified Spin and Gradient Echo (sSAGE) Approach for Human Brain Tumor Perfusion Imaging,” Magnetic Resonance Imaging 34, no. 9 (2016): 1248–1255.
Z. Q. Li, D. H. Wang, M. B. Ooi, et al., “A 3D Dual‐Echo Spiral Sequence for Simultaneous Dynamic Susceptibility Contrast and Dynamic Contrast‐Enhanced MRI With Single Bolus Injection,” Magnetic Resonance in Medicine 92, no. 2 (2024): 631–644.
J. Zhang, J. Wu, S. J. Chen, et al., “Robust Single‐Shot T2 Mapping via Multiple Overlapping‐Echo Acquisition and Deep Neural Network,” IEEE Transactions on Medical Imaging 38, no. 8 (2019): 1801–1811.
Q. Q. Yang, Y. H. Lin, J. C. Wang, et al., “Model‐Based Synthetic Data‐Driven Learning (MOST‐DL): Application in Single‐Shot T2 Mapping With Severe Head Motion Using Overlapping‐Echo Acquisition,” IEEE Transactions on Medical Imaging 41, no. 11 (2022): 3167–3181.
L. C. Ma, J. Wu, Q. Q. Yang, et al., “Single‐Shot Multi‐Parametric Mapping Based on Multiple Overlapping‐Echo Detachment (MOLED) Imaging,” NeuroImage 263 (2022): 119645.
Q. Q. Yang, L. C. Ma, Z. H. Zhou, et al., “Rapid High‐Fidelity T2* Mapping Using Single‐Shot Overlapping‐Echo Acquisition and Deep Learning Reconstruction,” Magnetic Resonance in Medicine 89, no. 6 (2023): 2157–2170.
M. Weigel, “Extended Phase Graphs: Dephasing, RF Pulses, and Echoes ‐ Pure and Simple,” Journal of Magnetic Resonance Imaging 41, no. 2 (2015): 266–295.
C. B. Cai, C. Wang, Y. Q. Zeng, et al., “Single‐Shot T2 Mapping Using Overlapping‐Echo Detachment Planar Imaging and a Deep Convolutional Neural Network,” Magnetic Resonance in Medicine 80, no. 5 (2018): 2202–2214.
Q. Q. Yang, Z. Wang, K. Y. Guo, C. B. Cai, and X. B. Qu, “Physics‐Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The Imaging Physics‐Based Data Synthesis Paradigm for Artificial Intelligence,” IEEE Signal Processing Magazine 40, no. 2 (2023): 129–140.
F. Liu, J. V. Velikina, W. F. Block, R. Kijowski, and A. A. Samsonov, “Fast Realistic MRI Simulations Based on Generalized Multi‐Pool Exchange Tissue Model,” IEEE Transactions on Medical Imaging 36, no. 2 (2017): 527–537.
T. Stöcker, K. Vahedipour, D. Pflugfelder, and N. J. Shah, “High‐Performance Computing MRI Simulations,” Magnetic Resonance in Medicine 64, no. 1 (2010): 186–193.
Q. Q. Yang, H. T. Huang, H. T. Yong, et al., “SMRI: Next‐Generation MRI Simulation Platform for Training Data Generation in the Era of AI,” in Proceedings of the 33rd Joint ISMRM & ISMRT Annual Meeting, Honolulu, Hawaii, US, 0009.
O. Ronneberger, P. Fischer, T. Brox, and T. Brox, “U‐Net: Convolutional Networks for Biomedical Image Segmentation,” 18th International Conference on Medical Image Computing and Computer‐Assisted Intervention (MICCAI), Munich, Germany, October 05–09, 2015, 234–241.
J. Johnson, A. Alahi, and F. F. Li, “Perceptual Losses for Real‐Time Style Transfer and Super‐Resolution,” 14th European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 08–16, 2016, 694–711.
J. Pintaske, P. Martirosian, H. Graf, et al., “Relaxivity of Gadopentetate Dimeglumine (Magnevist), Gadobutrol (Giadovist), and Gadobenate Dimeglumine (MultiHance) in Human Blood Plasma at 0.2, 1.5, and 3 Tesla,” Investigative Radiology 41, no. 3 (2006): 213–221.
P. S. Tofts, G. Brix, D. L. Buckley, et al., “Estimating Kinetic Parameters From Dynamic Contrast‐Enhanced T1‐Weighted MRI of a Diffusable Tracer: Standardized Quantities and Symbols,” Journal of Magnetic Resonance Imaging 10, no. 3 (1999): 223–232.
A. M. Stokes, N. Semmineh, and C. C. Quarles, “Validation of a T1 and T2* Leakage Correction Method Based on Multiecho Dynamic Susceptibility Contrast MRI Using MION as a Reference Standard,” Magnetic Resonance in Medicine 76, no. 2 (2016): 613–625.
B. F. Kjolby, L. Ostergaard, and V. G. Kiselev, “Theoretical Model of Intravascular Paramagnetic Tracers Effect on Tissue Relaxation,” Magnetic Resonance in Medicine 56, no. 1 (2006): 187–197.
O. Wu, L. Ostergaard, R. M. Weisskoff, T. Benner, B. R. Rosen, and A. G. Sorensen, “Tracer Arrival Timing‐Insensitive Technique for Estimating Flow in MR Perfusion‐Weighted Imaging Using Singular Value Decomposition With a Block‐Circulant Deconvolution Matrix,” Magnetic Resonance in Medicine 50, no. 1 (2003): 164–174.
A. Bjornerud, A. G. Sorensen, K. Mouridsen, and K. E. Emblem, “T1‐ and T2*‐Dominant Extravasation Correction in DSC‐MRI: Part I‐Theoretical Considerations and Implications for Assessment of Tumor Hemodynamic Properties,” Journal of Cerebral Blood Flow and Metabolism 31, no. 10 (2011): 2041–2053.
L. Wang, Q. Q. Yang, P. J. Zhang, et al., “A Versatile End‐To‐End Deep Learning Framework for Improved Full‐Automatic MRI Hemodynamic Parameter Estimation,” Biomedical Signal Processing and Control 108 (2025): 60.
L. Ostergaard, R. M. Weisskoff, D. A. Chesler, C. Gyldensted, and B. R. Rosen, “High Resolution Measurement of Cerebral Blood Flow Using Intravascular Tracer Bolus Passages .1. Mathematical Approach and Statistical Analysis,” Magnetic Resonance in Medicine 36, no. 5 (1996): 715–725.
D. G. Nishimura, P. Irarrazabal, and C. H. Meyer, “A Velocity k‐Space Analysis of Flow Effects in Echo‐Planar and Spiral Imaging,” Magnetic Resonance in Medicine 33, no. 4 (1995): 549–556.
B. Bourassa‐Moreau, R. Lebel, G. Gilbert, D. Mathieu, and M. Lepage, “Increased Precision in the Intravascular Arterial Input Function With Flow Compensation,” Magnetic Resonance in Medicine 82, no. 5 (2019): 1782–1795.
R. Turner and D. Lebihan, “Single‐Shot Diffusion Imaging at 2.0 Tesla,” Journal of Magnetic Resonance 86, no. 3 (1990): 445–452.
J. Mattiello, P. J. Basser, and D. LeBihan, “The b Matrix in Diffusion Tensor Echo‐Planar Imaging,” Magnetic Resonance in Medicine 37, no. 2 (1997): 292–300.
K. Setsompop, B. A. Gagoski, J. R. Polimeni, T. Witzel, V. J. Wedeen, and L. L. Wald, “Blipped‐Controlled Aliasing in Parallel Imaging for Simultaneous Multislice Echo Planar Imaging With Reduced g‐Factor Penalty,” Magnetic Resonance in Medicine 67, no. 5 (2012): 1210–1224.
M. K. Manhard, B. Bilgic, C. Y. Liao, et al., “Accelerated Whole‐Brain Perfusion Imaging Using a Simultaneous Multislice Spin‐Echo and Gradient‐Echo Sequence With Joint Virtual Coil Reconstruction,” Magnetic Resonance in Medicine 82, no. 3 (2019): 973–983.
H. T. Huang, Q. Q. Yang, J. C. Wang, P. J. Zhang, S. H. Cai, and C. B. Cai, “High‐Efficient Bloch Simulation of Magnetic Resonance Imaging Sequences Based on Deep Learning,” Physics in Medicine and Biology 68, no. 8 (2023): 49.
Grant Information: 2022YFC2402101 National Key R & D Program of China; 2022YFC2402102 National Key R & D Program of China; 2021Y9154 Science and Technology Project of Fujian Province of China; 12375291 National Natural Science Foundation of China; 22161142024 National Natural Science Foundation of China; 82071913 National Natural Science Foundation of China
Contributed Indexing: Keywords: ... mapping; DCE; DSC; MOLED; T2 mapping
Entry Date(s): Date Created: 20251102 Date Completed: 20260128 Latest Revision: 20260128
Update Code: 20260130
DOI: 10.1002/mrm.70165
PMID: 41177950
Βάση Δεδομένων: MEDLINE