Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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Fast Real-Time Cardiac MRI: a Review of Current Techniques and Future Directions

  • Wang, Xiaoqing (Institute for Diagnostic and Interventional Radiology, University Medical Center Gottingen) ;
  • Uecker, Martin (Institute for Diagnostic and Interventional Radiology, University Medical Center Gottingen) ;
  • Feng, Li (Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai)
  • Received : 2021.10.08
  • Accepted : 2021.11.12
  • Published : 2021.12.30
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Abstract

Cardiac magnetic resonance imaging (MRI) serves as a clinical gold-standard non-invasive imaging technique for the assessment of global and regional cardiac function. Conventional cardiac MRI is limited by the long acquisition time, the need for ECG gating and/or long breathhold, and insufficient spatiotemporal resolution. Real-time cardiac cine MRI refers to high spatiotemporal cardiac imaging using data acquired continuously without synchronization or binning, and therefore of potential interest in overcoming the limitations of conventional cardiac MRI. Novel acquisition and reconstruction techniques must be employed to facilitate real-time cardiac MRI. The goal of this study is to discuss methods that have been developed for real-time cardiac MRI. In particular, we classified existing techniques into two categories based on the use of non-iterative and iterative reconstruction. In addition, we present several research trends in this direction, including deep learning-based image reconstruction and other advanced real-time cardiac MRI strategies that reconstruct images acquired from real-time free-breathing techniques.

Keywords

References

  1. Lee DC, Markl M, Dall'Armellina E, et al. The growth and evolution of cardiovascular magnetic resonance: a 20-year history of the Society for Cardiovascular Magnetic Resonance (SCMR) annual scientific sessions. J Cardiovasc Magn Reson 2018;20:8 https://doi.org/10.1186/s12968-018-0429-z
  2. Lanzer P, Botvinick EH, Schiller NB, et al. Cardiac imaging using gated magnetic resonance. Radiology 1984;150:121-127 https://doi.org/10.1148/radiology.150.1.6227934
  3. Frahm J, Voit D, Uecker M. Real-time magnetic resonance imaging: radial gradient-echo sequences with nonlinear inverse reconstruction. Invest Radiol 2019;54:757-766 https://doi.org/10.1097/RLI.0000000000000584
  4. Nayak KS, Lim Y, Campbell-Washburn AE, Steeden J. Real-time magnetic resonance imaging. J Magn Reson Imaging 2020
  5. Dietz B, Fallone BG, Wachowicz K. Nomenclature for real-time magnetic resonance imaging. Magn Reson Med 2019;81:1483-1484 https://doi.org/10.1002/mrm.27487
  6. Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38:591-603 https://doi.org/10.1002/mrm.1910380414
  7. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42:952-962 https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S
  8. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47:1202-1210 https://doi.org/10.1002/mrm.10171
  9. Pruessmann KP, Weiger M, Boesiger P. Sensitivity encoded cardiac MRI. J Cardiovasc Magn Reson 2001;3:1-9 https://doi.org/10.1081/JCMR-100000143
  10. Kellman P, Epstein FH, McVeigh ER. Adaptive sensitivity encoding incorporating temporal filtering (TSENSE). Magn Reson Med 2001;45:846-852 https://doi.org/10.1002/mrm.1113
  11. Breuer FA, Kellman P, Griswold MA, Jakob PM. Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA). Magn Reson Med 2005;53:981-985 https://doi.org/10.1002/mrm.20430
  12. Madore B, Glover GH, Pelc NJ. Unaliasing by fourier-encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI. Magn Reson Med 1999;42:813-828 https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<813::AID-MRM1>3.0.CO;2-S
  13. Tsao J, Boesiger P, Pruessmann KP. k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med 2003;50:1031-1042 https://doi.org/10.1002/mrm.10611
  14. Pedersen H, Kozerke S, Ringgaard S, Nehrke K, Kim WY. k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis. Magn Reson Med 2009;62:706-716 https://doi.org/10.1002/mrm.22052
  15. Brummer ME, Moratal-Perez D, Hong CY, Pettigrew RI, Millet-Roig J, Dixon WT. Noquist: reduced field-of-view imaging by direct Fourier inversion. Magn Reson Med 2004;51:331-342 https://doi.org/10.1002/mrm.10694
  16. Malik SJ, Schmitz S, O'Regan D, Larkman DJ, Hajnal JV. x-f Choice: reconstruction of undersampled dynamic MRI by data-driven alias rejection applied to contrast-enhanced angiography. Magn Reson Med 2006;56:811-823 https://doi.org/10.1002/mrm.21008
  17. Huang F, Akao J, Vijayakumar S, Duensing GR, Limkeman M. k-t GRAPPA: a k-space implementation for dynamic MRI with high reduction factor. Magn Reson Med 2005;54:1172-1184 https://doi.org/10.1002/mrm.20641
  18. Tsao J, Kozerke S. MRI temporal acceleration techniques. J Magn Reson Imaging 2012;36:543-560 https://doi.org/10.1002/jmri.23640
  19. Lauterbur PC. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 1973;242:190-191 https://doi.org/10.1038/242190a0
  20. Ahn CB, Kim JH, Cho ZH. High-speed spiral-scan echo planar NMR imaging-I. IEEE Trans Med Imaging 1986;5:2-7 https://doi.org/10.1109/TMI.1986.4307732
  21. Uecker M, Lai P, Murphy MJ, et al. ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med 2014;71:990-1001 https://doi.org/10.1002/mrm.24751
  22. Seiberlich N, Ehses P, Duerk J, Gilkeson R, Griswold M. Improved radial GRAPPA calibration for real-time free-breathing cardiac imaging. Magn Reson Med 2011;65:492-505 https://doi.org/10.1002/mrm.22618
  23. Seiberlich N, Lee G, Ehses P, Duerk JL, Gilkeson R, Griswold M. Improved temporal resolution in cardiac imaging using through-time spiral GRAPPA. Magn Reson Med 2011;66:1682-1688 https://doi.org/10.1002/mrm.22952
  24. Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43:682-690 https://doi.org/10.1002/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G
  25. Pruessmann KP, Weiger M, Bornert P, Boesiger P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Med 2001;46:638-651 https://doi.org/10.1002/mrm.1241
  26. Ying L, Sheng J. Joint image reconstruction and sensitivity estimation in SENSE (JSENSE). Magn Reson Med 2007;57:1196-1202 https://doi.org/10.1002/mrm.21245
  27. Uecker M, Hohage T, Block KT, Frahm J. Image reconstruction by regularized nonlinear inversion--joint estimation of coil sensitivities and image content. Magn Reson Med 2008;60:674-682 https://doi.org/10.1002/mrm.21691
  28. Bakushinsky AB, Kokurin MY. Iterative methods for approximate solution of inverse problems. Mathematics and its applications. Springer Science & Business Media, 2005
  29. Engl HW, Hanke M, Neubauer A. Regularization of inverse problems (Vol. 375). Springer Science & Business Media, 1996
  30. Uecker M, Zhang S, Voit D, Karaus A, Merboldt KD, Frahm J. Real-time MRI at a resolution of 20 ms. NMR Biomed 2010;23:986-994 https://doi.org/10.1002/nbm.1585
  31. Wajer FTAW, Pruessmann KP. Major speedup of reconstruction for sensitivity encoding with arbitrary trajectories. In Proc Intl Soc Mag Res Med, 2001:767
  32. Uecker M, Zhang S, Frahm J. Nonlinear inverse reconstruction for real-time MRI of the human heart using undersampled radial FLASH. Magn Reson Med 2010;63:1456-1462 https://doi.org/10.1002/mrm.22453
  33. Zhang S, Uecker M, Voit D, Merboldt KD, Frahm J. Real-time cardiovascular magnetic resonance at high temporal resolution: radial FLASH with nonlinear inverse reconstruction. J Cardiovasc Magn Reson 2010;12:39 https://doi.org/10.1186/1532-429X-12-39
  34. Unterberg-Buchwald C, Ritter CO, Reupke V, et al. Targeted endomyocardial biopsy guided by real-time cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2017;19:45 https://doi.org/10.1186/s12968-017-0357-3
  35. Campbell-Washburn AE, Tavallaei MA, Pop M, et al. Real-time MRI guidance of cardiac interventions. J Magn Reson Imaging 2017;46:935-950 https://doi.org/10.1002/jmri.25749
  36. Backhaus SJ, Lange T, George EF, et al. Exercise stress real-time cardiac magnetic resonance imaging for noninvasive characterization of heart failure with preserved ejection fraction: the HFpEF-Stress trial. Circulation 2021;143:1484-1498 https://doi.org/10.1161/CIRCULATIONAHA.120.051542
  37. Schaetz S, Voit D, Frahm J, Uecker M. Accelerated computing in magnetic resonance imaging: real-time imaging using nonlinear inverse reconstruction. Comput Math Methods Med 2017;2017:3527269
  38. Block KT, Uecker M, Frahm J. Model-based iterative reconstruction for radial fast spin-echo MRI. IEEE Trans Med Imaging 2009;28:1759-1769 https://doi.org/10.1109/TMI.2009.2023119
  39. Fessler JA. Model-based image reconstruction for MRI. IEEE Signal Process Mag 2010;27:81-89 https://doi.org/10.1109/MSP.2010.936726
  40. Tan Z, Roeloffs V, Voit D, et al. Model-based reconstruction for real-time phase-contrast flow MRI: improved spatiotemporal accuracy. Magn Reson Med 2017;77:1082-1093 https://doi.org/10.1002/mrm.26192
  41. Wang X, Roeloffs V, Klosowski J, et al. Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH. Magn Reson Med 2018;79:730-740 https://doi.org/10.1002/mrm.26726
  42. Wang X, Tan Z, Scholand N, Roeloffs V, Uecker M. Physics-based reconstruction methods for magnetic resonance imaging. Philos Trans A Math Phys Eng Sci 2021;379:20200196
  43. Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006;52:1289-1306 https://doi.org/10.1109/TIT.2006.871582
  44. Candes EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006;52:489-509 https://doi.org/10.1109/TIT.2005.862083
  45. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182-1195 https://doi.org/10.1002/mrm.21391
  46. Block KT, Uecker M, Frahm J. Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magn Reson Med 2007;57:1086-1098 https://doi.org/10.1002/mrm.21236
  47. Gamper U, Boesiger P, Kozerke S. Compressed sensing in dynamic MRI. Magn Reson Med 2008;59:365-373 https://doi.org/10.1002/mrm.21477
  48. Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. Compressed sensing for body MRI. J Magn Reson Imaging 2017;45:966-987 https://doi.org/10.1002/jmri.25547
  49. Liang D, Liu B, Wang J, Ying L. Accelerating SENSE using compressed sensing. Magn Reson Med 2009;62:1574-1584 https://doi.org/10.1002/mrm.22161
  50. Otazo R, Kim D, Axel L, Sodickson DK. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 2010;64:767-776 https://doi.org/10.1002/mrm.22463
  51. Lustig M, Santos JM, Donoho DL, Pauly JM. k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In Proceedings of the 13th Annual Meeting of ISMRM, 2006:2420
  52. Usman M, Prieto C, Schaeffter T, Batchelor PG. k-t Group sparse: a method for accelerating dynamic MRI. Magn Reson Med 2011;66:1163-1176 https://doi.org/10.1002/mrm.22883
  53. Ting ST, Ding Y, Giri S, Jin N, Simonetti OP, Ahmad R. Sub-30 ms real-time, free-breathing cardiac imaging with SPIRiT. J Cardiovasc Magn Reson 2014;16:1-3 https://doi.org/10.1186/1532-429X-16-1
  54. Ting ST, Ahmad R, Jin N, et al. Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model. Magn Reson Med 2017;77:1505-1515 https://doi.org/10.1002/mrm.26224
  55. Ye JC. Compressed sensing MRI: a review from signal processing perspective. BMC Biomed Eng 2019;1:1-17 https://doi.org/10.1186/s42490-019-0004-1
  56. Feng L, Srichai MB, Lim RP, et al. Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE. Magn Reson Med 2013;70:64-74 https://doi.org/10.1002/mrm.24440
  57. Hager W, Zhang H. A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J Optim 2005;16:170-192 https://doi.org/10.1137/030601880
  58. Daubechies I, Defrise M, De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math 2004;57:1413-1457 https://doi.org/10.1002/cpa.20042
  59. Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2009;2:183-202 https://doi.org/10.1137/080716542
  60. Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 2011;3:1-122 https://doi.org/10.1561/2200000016
  61. Liu J, Rapin J, Chang TC, Lefebvre A, Zenge M, Mueller E, Nadar MS. Dynamic cardiac MRI reconstruction with weighted redundant Haar wavelets. In Proceedings of the 20th Annual Meeting of the ISMRM, 2012:4249
  62. Qu X, Hou Y, Lam F, Guo D, Zhong J, Chen Z. Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator. Med Image Anal 2014;18:843-856 https://doi.org/10.1016/j.media.2013.09.007
  63. Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 2006;54:4311-4322 https://doi.org/10.1109/TSP.2006.881199
  64. Ravishankar S, Bresler Y. MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans Med Imaging 2011;30:1028-1041 https://doi.org/10.1109/TMI.2010.2090538
  65. Caballero J, Price AN, Rueckert D, Hajnal JV. Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Trans Med Imaging 2014;33:979-994 https://doi.org/10.1109/TMI.2014.2301271
  66. Brinegar C, Wu YJ, Foley LM, et al. Real-time cardiac MRI without triggering, gating, or breath holding. Annu Int Conf IEEE Eng Med Biol Soc 2008;2008:3381-3384
  67. Zhao B, Haldar JP, Brinegar C, Liang ZP. Low rank matrix recovery for real-time cardiac MRI. Proc IEEE Int Symp Biomed Imaging, 2010:996-999
  68. Lingala SG, Hu Y, DiBella E, Jacob M. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging 2011;30:1042-1054 https://doi.org/10.1109/TMI.2010.2100850
  69. Zhao B, Haldar JP, Christodoulou AG, Liang ZP. Image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. IEEE Trans Med Imaging 2012;31:1809-1820 https://doi.org/10.1109/TMI.2012.2203921
  70. Liang ZP. Spatiotemporal imaging with partially separable functions. Proc IEEE Int Symp Biomed Imaging, 2007:988-991
  71. Batchelor PG, Atkinson D, Irarrazaval P, Hill DL, Hajnal J, Larkman D. Matrix description of general motion correction applied to multishot images. Magn Reson Med 2005;54:1273-1280 https://doi.org/10.1002/mrm.20656
  72. Hansen MS, Sorensen TS, Arai AE, Kellman P. Retrospective reconstruction of high temporal resolution cine images from real-time MRI using iterative motion correction. Magn Reson Med 2012;68:741-750 https://doi.org/10.1002/mrm.23284
  73. Xue H, Kellman P, LaRocca G, Arai AE, Hansen MS. High spatial and temporal resolution retrospective cine cardiovascular magnetic resonance from shortened free breathing real-time acquisitions. J Cardiovasc Magn Reson 2013; 15:1-15 https://doi.org/10.1186/1532-429X-15-1
  74. Usman M, Atkinson D, Odille F, et al. Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magn Reson Med 2013;70:504-516 https://doi.org/10.1002/mrm.24463
  75. Li H, Haltmeier M, Zhang S, Frahm J, Munk A. Aggregated motion estimation for real-time MRI reconstruction. Magn Reson Med 2014;72:1039-1048 https://doi.org/10.1002/mrm.25020
  76. Feng L, Grimm R, Block KT, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 2014;72:707-717 https://doi.org/10.1002/mrm.24980
  77. Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R. XD-GRASP: golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 2016;75:775-788 https://doi.org/10.1002/mrm.25665
  78. Poddar S, Jacob M. Dynamic MRI using smoothness regularization on manifolds (SToRM). IEEE Trans Med Imaging 2016;35:1106-1115 https://doi.org/10.1109/TMI.2015.2509245
  79. Christodoulou AG, Shaw JL, Nguyen C, et al. Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging. Nat Biomed Eng 2018;2:215-226 https://doi.org/10.1038/s41551-018-0217-y
  80. Shaw JL, Yang Q, Zhou Z, et al. Free-breathing, non-ECG, continuous myocardial T1 mapping with cardiovascular magnetic resonance multitasking. Magn Reson Med 2019;81:2450-2463 https://doi.org/10.1002/mrm.27574
  81. Wang N, Gaddam S, Wang L, et al. Six-dimensional quantitative DCE MR multitasking of the entire abdomen: method and application to pancreatic ductal adenocarcinoma. Magn Reson Med 2020;84:928-948 https://doi.org/10.1002/mrm.28167
  82. Cheng JY, Hanneman K, Zhang T, et al. Comprehensive motion-compensated highly accelerated 4D flow MRI with ferumoxytol enhancement for pediatric congenital heart disease. J Magn Reson Imaging 2016;43:1355-1368 https://doi.org/10.1002/jmri.25106
  83. Cheng JY, Zhang T, Alley MT, et al. Comprehensive multidimensional MRI for the simultaneous assessment of cardiopulmonary anatomy and physiology. Sci Rep 2017;7:5330 https://doi.org/10.1038/s41598-017-04676-8
  84. Winkelmann S, Schaeffter T, Koehler T, Eggers H, Doessel O. An optimal radial profile order based on the golden ratio for time-resolved MRI. IEEE Trans Med Imaging 2007;26:68-76 https://doi.org/10.1109/TMI.2006.885337
  85. Larson AC, White RD, Laub G, McVeigh ER, Li D, Simonetti OP. Self-gated cardiac cine MRI. Magn Reson Med 2004;51:93-102 https://doi.org/10.1002/mrm.10664
  86. Rosenzweig S, Scholand N, Holme HCM, Uecker M. Cardiac and respiratory self-gating in radial MRI using an adapted singular spectrum analysis (SSA-FARY). IEEE Trans Med Imaging 2020;39:3029-3041 https://doi.org/10.1109/tmi.2020.2985994
  87. Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imaging via deep learning. Proc IEEE Int Symp Biomed Imaging 2016;2016:514-517
  88. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018;79:3055-3071 https://doi.org/10.1002/mrm.26977
  89. Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 2018;37:491-503 https://doi.org/10.1109/tmi.2017.2760978
  90. Aggarwal HK, Mani MP, Jacob M. MoDL: model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging 2019;38:394-405 https://doi.org/10.1109/tmi.2018.2865356
  91. Luo G, Zhao N, Jiang W, Hui ES, Cao P. MRI reconstruction using deep Bayesian estimation. Magn Reson Med 2020;84:2246-2261 https://doi.org/10.1002/mrm.28274
  92. Knoll F, Hammernik K, Zhang C, et al. Deep-learning methods for parallel magnetic resonance imaging reconstruction: a survey of the current approaches, trends, and issues. IEEE Signal Process Mag 2020;37:128-140
  93. He Z, Zhou P, Zhu H. Study of the interactivity between mercury and cellular system labeled with carboxymethyl chitosan-coated quantum dots and its application in a real-time in-situ detection of mercury. Spectrochim Acta A Mol Biomol Spectrosc 2015;139:179-183 https://doi.org/10.1016/j.saa.2014.12.049
  94. Ke Z, Zhu Y, Liang D. Cascaded residual dense networks for dynamic MR imaging with edge-enhanced loss constraint. Investig Magn Reson Imaging 2020;24:214-222 https://doi.org/10.13104/imri.2020.24.4.214
  95. Park SJ, Ahn CB. Blended-transfer learning for compressed-sensing cardiac CINE MRI. Investig Magn Reson Imaging 2021;25:10-22 https://doi.org/10.13104/imri.2021.25.1.10
  96. Hauptmann A, Arridge S, Lucka F, Muthurangu V, Steeden JA. Real-time cardiovascular MR with spatiotemporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med 2019;81:1143-1156 https://doi.org/10.1002/mrm.27480
  97. Wundrak S, Paul J, Ulrici J, et al. Golden ratio sparse MRI using tiny golden angles. Magn Reson Med 2016;75:2372-2378 https://doi.org/10.1002/mrm.25831