Objective: MR elastography (MRE) derives tissue shear stiffness from the wavelength of propagating shear waves. Geometric distortion can therefore alter the apparent wavelength and introduce errors in the calculated shear stiffness. This study evaluated how geometric distortion affects shear stiffness measurements in EPI-based MRE, with a focus on the phase-encoding orientation relative to the wave propagation direction and on the influence of vibration frequency. Materials and Methods: Phantom experiments were conducted with systematically varied phase-encoding directions and vibration frequencies to characterize distortion effects under controlled conditions. To assess distortion effects in a clinical setting, liver MRE was performed in three healthy volunteers. Distortion correction was applied in both experiments, and distortion effects were quantitatively evaluated by comparing shear stiffness values before and after correction. Results: The phantom experiments identified two factors that compromise measurement uniformity. First, the spatial uniformity of shear stiffness measurements decreased when the wave propagation direction was parallel to the phase-encoding direction (i.e., the direction along which distortion occurs). Second, this non-uniformity further increased as the propagating wavelength became shorter, which occurs at higher vibration frequencies or in softer tissue regions. In contrast, liver MRE in healthy volunteers showed minimal distortion effects (mean difference: 0.10 ± 0.15 kPa; 95% CI: ?0.28 to 0.48 kPa), suggesting that current clinical liver MRE protocols are largely unaffected by geometric distortion. Conclusion: As MRE is increasingly applied to organs beyond the liver, geometric distortion may become a more relevant source of error in these emerging applications. Careful consideration of wave propagation patterns and distortion characteristics is therefore essential for accurate and reliable MRE measurements across various organs.
References
[1]
Muthupillai, R., Lomas, D.J., Rossman, P.J., Greenleaf, J.F., Manduca, A. and Ehman, R.L. (1995) Magnetic Resonance Elastography by Direct Visualization of Propagating Acoustic Strain Waves. Science, 269, 1854-1857. https://doi.org/10.1126/science.7569924
[2]
Muthupillai, R. and Ehman, R.L. (1996) Magnetic Resonance Elastography. NatureMedicine, 2, 601-603. https://doi.org/10.1038/nm0596-601
[3]
Muthupillai, R., Rossman, P.J., Lomas, D.J., Greenleaf, J.F., Riederer, S.J. and Ehman, R.L. (1996) Magnetic Resonance Imaging of Transverse Acoustic Strain Waves. MagneticResonanceinMedicine, 36, 266-274. https://doi.org/10.1002/mrm.1910360214
[4]
Mariappan, Y.K., Glaser, K.J. and Ehman, R.L. (2010) Magnetic Resonance Elastography: A Review. ClinicalAnatomy, 23, 497-511. https://doi.org/10.1002/ca.21006
[5]
Yin, M. and Ehman, R.L. (2024) MR Elastography: Practical Questions, from the AJR Special Series on Imaging of Fibrosis. AmericanJournalofRoentgenology, 222, e2329437. https://doi.org/10.2214/ajr.23.29437
[6]
Yin, M., Talwalkar, J.A., Glaser, K.J., Manduca, A., Grimm, R.C., Rossman, P.J., et al. (2007) Assessment of Hepatic Fibrosis with Magnetic Resonance Elastography. ClinicalGastroenterologyandHepatology, 5, 1207-1213.e2. https://doi.org/10.1016/j.cgh.2007.06.012
[7]
Huwart, L., Sempoux, C., Vicaut, E., Salameh, N., Annet, L., Danse, E., et al. (2008) Magnetic Resonance Elastography for the Noninvasive Staging of Liver Fibrosis. Gastroenterology, 135, 32-40. https://doi.org/10.1053/j.gastro.2008.03.076
[8]
Huwart, L., Salameh, N., ter Beek, L., Vicaut, E., Peeters, F., Sinkus, R., et al. (2008) MR Elastography of Liver Fibrosis: Preliminary Results Comparing Spin-Echo and Echo-Planar Imaging. EuropeanRadiology, 18, 2535-2541. https://doi.org/10.1007/s00330-008-1051-5
[9]
Murphy, M.C., Huston, J., Jack, C.R., Glaser, K.J., Manduca, A., Felmlee, J.P., et al. (2011) Decreased Brain Stiffness in Alzheimer’s Disease Determined by Magnetic Resonance Elastography. JournalofMagneticResonanceImaging, 34, 494-498. https://doi.org/10.1002/jmri.22707
[10]
Murphy, M.C., Huston, J. and Ehman, R.L. (2019) MR Elastography of the Brain and Its Application in Neurological Diseases. NeuroImage, 187, 176-183. https://doi.org/10.1016/j.neuroimage.2017.10.008
[11]
Takamura, T., Motosugi, U., Sasaki, Y., Kakegawa, T., Sato, K., Glaser, K.J., et al. (2020) Influence of Age on Global and Regional Brain Stiffness in Young and Middle‐aged Adults. JournalofMagneticResonanceImaging, 51, 727-733. https://doi.org/10.1002/jmri.26881
[12]
Yin, Z., Sui, Y., Trzasko, J.D., Rossman, P.J., Manduca, A., Ehman, R.L., et al. (2018) In Vivo Characterization of 3D Skull and Brain Motion during Dynamic Head Vibration Using Magnetic Resonance Elastography. MagneticResonanceinMedicine, 80, 2573-2585. https://doi.org/10.1002/mrm.27347
[13]
Sinkus, R., Siegmann, K., Xydeas, T., Tanter, M., Claussen, C. and Fink, M. (2007) MR Elastography of Breast Lesions: Understanding the Solid/Liquid Duality Can Improve the Specificity of Contrast-Enhanced MR Mammography. MagneticResonanceinMedicine, 58, 1135-1144. https://doi.org/10.1002/mrm.21404
[14]
Patel, B.K., Pepin, K., Brandt, K.R., Mazza, G.L., Pockaj, B.A., Chen, J., et al. (2022) Association of Breast Cancer Risk, Density, and Stiffness: Global Tissue Stiffness on Breast MR Elastography (MRE). BreastCancerResearchandTreatment, 194, 79-89. https://doi.org/10.1007/s10549-022-06607-2
[15]
Patel, B.K., Samreen, N., Zhou, Y., Chen, J., Brandt, K., Ehman, R., et al. (2021) MR Elastography of the Breast: Evolution of Technique, Case Examples, and Future Directions. ClinicalBreastCancer, 21, e102-e111. https://doi.org/10.1016/j.clbc.2020.08.005
[16]
Rouvière, O., Souchon, R., Pagnoux, G., Ménager, J. and Chapelon, J. (2011) Magnetic Resonance Elastography of the Kidneys: Feasibility and Reproducibility in Young Healthy Adults. JournalofMagneticResonanceImaging, 34, 880-886. https://doi.org/10.1002/jmri.22670
[17]
Gandhi, D., Kalra, P., Raterman, B., Mo, X., Dong, H. and Kolipaka, A. (2020) Magnetic Resonance Elastography-Derived Stiffness of the Kidneys and Its Correlation with Water Perfusion. NMRinBiomedicine, 33, e4237. https://doi.org/10.1002/nbm.4237
[18]
Ishihara, Y., Numano, T., Ito, D., Nishijo, H., Takamoto, K., Kikuchi, J., et al. (2024) Development of a Suitable Vibration Pad for Renal MR Elastography. MagneticResonanceImaging, 109, 120-126. https://doi.org/10.1016/j.mri.2024.03.013
[19]
Song, Q., Shi, Y., Gao, F., Yin, M., Yang, R., Liu, Y., et al. (2022) Feasibility and Reproducibility of Multifrequency Magnetic Resonance Elastography in Healthy and Diseased Pancreases. JournalofMagneticResonanceImaging, 56, 1769-1780. https://doi.org/10.1002/jmri.28158
[20]
Xu, Y., Cai, X., Shi, Y., Yin, M., Lan, G., Zhang, X., et al. (2020) Normative Pancreatic Stiffness Levels and Related Influences Established by Magnetic Resonance Elastography in Volunteers. JournalofMagneticResonanceImaging, 52, 448-458. https://doi.org/10.1002/jmri.27052
[21]
Serai, S.D., Abu-El-Haija, M. and Trout, A.T. (2019) 3D MR Elastography of the Pancreas in Children. AbdominalRadiology, 44, 1834-1840. https://doi.org/10.1007/s00261-019-01903-w
[22]
Ito, D., Numano, T., Mizuhara, K., Takamoto, K., Onishi, T. and Nishijo, H. (2016) A New Technique for MR Elastography of the Supraspinatus Muscle: A Gradient-Echo Type Multi-Echo Sequence. MagneticResonanceImaging, 34, 1181-1188. https://doi.org/10.1016/j.mri.2016.06.003
[23]
Dresner, M.A., Rose, G.H., Rossman, P.J., Muthupillai, R., Manduca, A. and Ehman, R.L. (2001) Magnetic Resonance Elastography of Skeletal Muscle. JournalofMagneticResonanceImaging, 13, 269-276. https://doi.org/10.1002/1522-2586(200102)13:2<269::aid-jmri1039>3.0.co;2-1
[24]
Ito, D., Numano, T., Takamoto, K., Ueki, T., Habe, T., Igarashi, K., et al. (2019) Simultaneous Acquisition of Magnetic Resonance Elastography of the Supraspinatus and the Trapezius Muscles. MagneticResonanceImaging, 57, 95-102. https://doi.org/10.1016/j.mri.2018.11.011
[25]
Bensamoun, S.F., Ringleb, S.I., Littrell, L., Chen, Q., Brennan, M., Ehman, R.L., et al. (2006) Determination of Thigh Muscle Stiffness Using Magnetic Resonance Elastography. JournalofMagneticResonanceImaging, 23, 242-247. https://doi.org/10.1002/jmri.20487
[26]
Numano, T., Habe, T., Ito, D., Onishi, T., Takamoto, K., Mizuhara, K., et al. (2019) A New Technique for Motion Encoding Gradient-Less MR Elastography of the Psoas Major Muscle: A Gradient-Echo Type Multi-Echo Sequence. MagneticResonanceImaging, 63, 85-92. https://doi.org/10.1016/j.mri.2019.08.006
[27]
Hojo, E., Glaser, K.J., Harada-Hayashi, A., Le, Y., Chen, J., Hulshizer, T.C., et al. (2024) Feasibility Study of the Application of Magnetic Resonance Elastography (MRE) to Measure Oral Posture Related Changes in Stiffness of Zygomaticus Major Muscle. MagneticResonanceImaging, 111, 196-201. https://doi.org/10.1016/j.mri.2024.05.005
[28]
Yin, M., Glaser, K.J., Talwalkar, J.A., Chen, J., Manduca, A. and Ehman, R.L. (2016) Hepatic MR Elastography: Clinical Performance in a Series of 1377 Consecutive Examinations. Radiology, 278, 114-124. https://doi.org/10.1148/radiol.2015142141
[29]
Plaikner, M., Kremser, C., Zoller, H., Kannengiesser, S. and Henninger, B. (2021) MR Elastography in Patients with Suspected Diffuse Liver Disease at 1.5T: Intraindividual Comparison of Gradient-Recalled Echo versus Spin-Echo Echo-Planar Imaging Sequences and Investigation of Potential Confounding Factors. EuropeanJournalofRadiology, 142, Article ID: 109898. https://doi.org/10.1016/j.ejrad.2021.109898
[30]
Serai, S.D., Dillman, J.R. and Trout, A.T. (2017) Spin-Echo Echo-Planar Imaging MR Elastography versus Gradient-Echo MR Elastography for Assessment of Liver Stiffness in Children and Young Adults Suspected of Having Liver Disease. Radiology, 282, 761-770. https://doi.org/10.1148/radiol.2016160589
[31]
Zhan, C., Kannengiesser, S., Chandarana, H., Fenchel, M., Ream, J. and Shanbhogue, K.P. (2019) MR Elastography of Liver at 3 Tesla: Comparison of Gradient-Recalled Echo (GRE) and Spin-Echo (SE) Echo-Planar Imaging (EPI) Sequences and Agreement across Stiffness Measurements. AbdominalRadiology, 44, 1825-1833. https://doi.org/10.1007/s00261-019-01932-5
[32]
Jezzard, P. and Balaban, R.S. (1995) Correction for Geometric Distortion in Echo Planar Images from B0 Field Variations. MagneticResonanceinMedicine, 34, 65-73. https://doi.org/10.1002/mrm.1910340111
[33]
Kadah, Y.M. and Xiaoping Hu, (1998) Algebraic Reconstruction for Magnetic Resonance Imaging under B0 Inhomogeneity. IEEETransactionsonMedicalImaging, 17, 362-370. https://doi.org/10.1109/42.712126
[34]
Wu, M., Chang, L., Walker, L., Lemaitre, H., Barnett, A.S., Marenco, S., et al. (2008) Comparison of EPI Distortion Correction Methods in Diffusion Tensor MRI Using a Novel Framework. In: Metaxas, D., Axel, L., Fichtinger, G. and Székely, G., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008, Springer, 321-329. https://doi.org/10.1007/978-3-540-85990-1_39
[35]
Fehlner, A., Hirsch, S., Weygandt, M., Christophel, T., Barnhill, E., Kadobianskyi, M., et al. (2017) Increasing the Spatial Resolution and Sensitivity of Magnetic Resonance Elastography by Correcting for Subject Motion and Susceptibility-Induced Image Distortions. JournalofMagneticResonanceImaging, 46, 134-141. https://doi.org/10.1002/jmri.25516
[36]
McIlvain, G., McGarry, M.D.J. and Johnson, C.L. (2022) Quantitative Effects of Off‐resonance Related Distortion on Brain Mechanical Property Estimation with Magnetic Resonance Elastography. NMRinBiomedicine, 35, e4616. https://doi.org/10.1002/nbm.4616
[37]
Committee QMB (2022) Magnetic Resonance Elastography of the Liver, Quantitative Imaging Biomarkers Alliance. https://qibawiki.rsna.org/index.php/MRE_Biomarker_Ctte
[38]
Unser, M., Aldroubi, A. and Eden, M. (1993) B-Spline Signal Processing. I. Theory. IEEE Transactions on Signal Processing, 41, 821-833. https://doi.org/10.1109/78.193220
[39]
Unser, M., Aldroubi, A. and Eden, M. (1993) B-Spline Signal Processing. II. Efficiency Design and Applications. IEEE Transactions on Signal Processing, 41, 834-848. https://doi.org/10.1109/78.193221
[40]
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O. and Hawkes, D.J. (1999) Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEETransactionsonMedicalImaging, 18, 712-721. https://doi.org/10.1109/42.796284
[41]
Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T.K. and Eubank, W. (2003) PET-CT Image Registration in the Chest Using Free-Form Deformations. IEEE Transactions on MedicalImaging, 22, 120-128. https://doi.org/10.1109/tmi.2003.809072
[42]
Liu, D.C. and Nocedal, J. (1989) On the Limited Memory BFGS Method for Large Scale Optimization. MathematicalProgramming, 45, 503-528. https://doi.org/10.1007/bf01589116
[43]
Zhu, C., Byrd, R.H., Lu, P. and Nocedal, J. (1997) Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization. ACMTransactionsonMathematicalSoftware, 23, 550-560. https://doi.org/10.1145/279232.279236
[44]
Ito, D., Habe, T., Numano, T., Okuda, S., Soga, S. and Jinzaki, M. (2024) A Versatile MR Elastography Research Tool with a Modified Motion Signal-To-Noise Ratio Approach. MagneticResonanceinMedicalSciences, 23, 417-427. https://doi.org/10.2463/mrms.mp.2022-0149
[45]
Knutsson, H., Westin, C.F. and Granlund, G. (1994) Local Multiscale Frequency and Bandwidth Estimation. Proceedings of 1st International Conference on Image Processing, Austin, 13-16 November 199, 36-40. https://doi.org/10.1109/icip.1994.413270
[46]
Manduca, A., Muthupillai, R., Rossman, P.J., Greenleaf, J.F. and Ehman, R.L. (1996) Image Processing for Magnetic-Resonance Elastography. SPIE Proceedings, 2710, 616-623. https://doi.org/10.1117/12.237965