Log in

Advanced MRI techniques in abdominal imaging

  • Special Section: Editor’s Invite
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients’ breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Zaitsev M, Maclaren J, Herbst M. (2015) Motion artifacts in MRI: A complex problem with many partial solutions. Journal of magnetic resonance imaging: JMRI 42:887–901. https://doi.org/10.1002/jmri.24850

    Article  PubMed  Google Scholar 

  2. Glockner JF, Hu HH, Stanley DW, Angelos L, King K. (2005) Parallel MR Imaging: A User’s Guide. RadioGraphics 25:1279–1297. https://doi.org/10.1148/rg.255045202

    Article  PubMed  Google Scholar 

  3. Deshmane A, Gulani V, Griswold MA, Seiberlich N. (2012) Parallel MR imaging. J Magn Reson Imaging 36:55–72. https://doi.org/10.1002/jmri.23639

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kwok WE. (2022) Basic Principles of and Practical Guide to Clinical MRI Radiofrequency Coils. Radiographics: a review publication of the Radiological Society of North America, Inc 42:898–918. https://doi.org/10.1148/rg.210110

    Article  PubMed  Google Scholar 

  5. Zhang L, Kholmovski EG, Guo J, Choi SE, Morrell GR, Parker DL. (2009) HASTE sequence with parallel acquisition and T2 decay compensation: application to carotid artery imaging. Magnetic resonance imaging 27:13–22. https://doi.org/10.1016/j.mri.2008.05.009

    Article  PubMed  Google Scholar 

  6. Yanasak NE, Kelly MJ. (2014) MR Imaging Artifacts and Parallel Imaging Techniques with Calibration Scanning: A New Twist on Old Problems. RadioGraphics 34:532–548. https://doi.org/10.1148/rg.342135051

    Article  PubMed  Google Scholar 

  7. Sheng J, Shi Y, Zhang Q. (2021) Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling. Scientific Reports 11:9005. https://doi.org/10.1038/s41598-021-88567-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hoge WS, Brooks DH. (2008) Using GRAPPA to improve autocalibrated coil sensitivity estimation for the SENSE family of parallel imaging reconstruction algorithms. Magnetic Resonance in Medicine 60:462–467. https://doi.org/10.1002/mrm.21634

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cummings E, Macdonald JA, Seiberlich N. (2022) Chap. 6 - Parallel Imaging. In: Akçakaya M, Doneva M, Prieto C, (eds) Advances in Magnetic Resonance Technology and Applications. Academic Press, pp 129–157

  10. Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG. (2008) Diffusion Tensor MR Imaging and Fiber Tractography: Technical Considerations. American Journal of Neuroradiology 29:843. https://doi.org/10.3174/ajnr.A1052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Huh J, Kim SY, Yeh BM, Lee SS, Kim KW, Wu EH, et al. (2015) Troubleshooting Arterial-Phase MR Images of Gadoxetate Disodium-Enhanced Liver. Korean J Radiol 16:1207–1215. https://doi.org/10.3348/kjr.2015.16.6.1207

    Article  PubMed  PubMed Central  Google Scholar 

  12. Skare S, Newbould RD, Clayton DB, Albers GW, Nagle S, Bammer R. (2007) Clinical multishot DW-EPI through parallel imaging with considerations of susceptibility, motion, and noise. Magn Reson Med 57:881–890. https://doi.org/10.1002/mrm.21176

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yoon JH, Nickel MD, Peeters JM, Lee JM. (2019) Rapid Imaging: Recent Advances in Abdominal MRI for Reducing Acquisition Time and Its Clinical Applications. Korean J Radiol 20:1597–1615. https://doi.org/10.3348/kjr.2018.0931

    Article  PubMed  PubMed Central  Google Scholar 

  14. Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, et al. (2014) Golden-angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magnetic Resonance in Medicine 72:707–717. https://doi.org/10.1002/mrm.24980

    Article  PubMed  Google Scholar 

  15. Winkelmann S, Schaeffter T, Koehler T, Eggers H, Doessel O. (2007) An optimal radial profile order based on the Golden Ratio for time-resolved MRI. IEEE Trans Med Imaging 26:68–76. https://doi.org/10.1109/tmi.2006.885337

    Article  PubMed  Google Scholar 

  16. Yoon JH, Lee JM, Yu MH, Hur BY, Grimm R, Block KT, et al. (2018) Evaluation of Transient Motion During Gadoxetic Acid-Enhanced Multiphasic Liver Magnetic Resonance Imaging Using Free-Breathing Golden-Angle Radial Sparse Parallel Magnetic Resonance Imaging. Investigative radiology 53:52–61. https://doi.org/10.1097/rli.0000000000000409

    Article  PubMed  PubMed Central  Google Scholar 

  17. Chandarana H, Block KT, Winfeld MJ, Lala SV, Mazori D, Giuffrida E, et al. (2014) Free-breathing contrast-enhanced T1-weighted gradient-echo imaging with radial k-space sampling for paediatric abdominopelvic MRI. Eur Radiol 24:320–326. https://doi.org/10.1007/s00330-013-3026-4

    Article  PubMed  Google Scholar 

  18. Hirokawa Y, Isoda H, Maetani YS, Arizono S, Shimada K, Togashi K. (2008) MRI Artifact Reduction and Quality Improvement in the Upper Abdomen with PROPELLER and Prospective Acquisition Correction (PACE) Technique. American Journal of Roentgenology 191:1154–1158. https://doi.org/10.2214/AJR.07.3657

    Article  PubMed  Google Scholar 

  19. Breuer FA, Blaimer M, Mueller MF, Seiberlich N, Heidemann RM, Griswold MA, et al. (2006) Controlled aliasing in volumetric parallel imaging (2D CAIPIRINHA). Magnetic Resonance in Medicine 55:549–556. https://doi.org/10.1002/mrm.20787

    Article  PubMed  Google Scholar 

  20. Park YS, Lee CH, Kim IS, Kiefer B, Woo ST, Kim KA, et al. (2014) Usefulness of controlled aliasing in parallel imaging results in higher acceleration in gadoxetic acid-enhanced liver magnetic resonance imaging to clarify the hepatic arterial phase. Investigative radiology 49:183–188. https://doi.org/10.1097/rli.0000000000000011

    Article  PubMed  Google Scholar 

  21. Kim B, Lee CK, Seo N, Lee SS, Kim JK, Choi Y, et al. (2016) Comparison of CAIPIRINHA-VIBE, Radial-VIBE, and conventional VIBE sequences for dynamic contrast-enhanced (DCE) MRI: A validation study using a DCE-MRI phantom. Magnetic resonance imaging 34:638–644. https://doi.org/10.1016/j.mri.2015.11.011

    Article  PubMed  Google Scholar 

  22. Seo N, Park SJ, Kim B, Lee CK, Huh J, Kim JK, et al. (2016) Feasibility of free-breathing dynamic contrast-enhanced MRI of the abdomen: a comparison between CAIPIRINHA-VIBE, Radial-VIBE with KWIC reconstruction and conventional VIBE. British Journal of Radiology 89:20160150. https://doi.org/10.1259/bjr.20160150

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wright KL, Harrell MW, Jesberger JA, Landeras L, Nakamoto DA, Thomas S, et al. (2014) Clinical evaluation of CAIPIRINHA: comparison against a GRAPPA standard. J Magn Reson Imaging 39:189–194. https://doi.org/10.1002/jmri.24105

    Article  PubMed  Google Scholar 

  24. Yu MH, Lee JM, Yoon J-H, Kiefer B, Han JK, Choi B-I. (2013) Clinical application of controlled aliasing in parallel imaging results in a higher acceleration (CAIPIRINHA)-volumetric interpolated breathhold (VIBE) sequence for gadoxetic acid-enhanced liver MR imaging. Journal of Magnetic Resonance Imaging 38:1020–1026. https://doi.org/10.1002/jmri.24088

    Article  PubMed  Google Scholar 

  25. Yutzy SR, Seiberlich N, Duerk JL, Griswold MA. (2011) Improvements in multislice parallel imaging using radial CAIPIRINHA. Magn Reson Med 65:1630–1637. https://doi.org/10.1002/mrm.22752

    Article  PubMed  PubMed Central  Google Scholar 

  26. Morani AC, Vicens RA, Wei W, Gupta S, Vikram R, Balachandran A, et al. (2015) CAIPIRINHA-VIBE and GRAPPA-VIBE for liver MRI at 1.5 T: a comparative in vivo patient study. J Comput Assist Tomogr 39:263–269. https://doi.org/10.1097/rct.0000000000000200

    Article  PubMed  PubMed Central  Google Scholar 

  27. Donoho DL. (2006) Compressed sensing. IEEE Transactions on Information Theory 52:1289–1306. https://doi.org/10.1109/TIT.2006.871582

    Article  Google Scholar 

  28. Yoon S, Park SH, Han D. (2023) Uncover This Tech Term: Compressed Sensing Magnetic Resonance Imaging. Korean J Radiol 24:1293–1302. http://doi.org/10.3348/kjr.2023.0743

    Article  PubMed  PubMed Central  Google Scholar 

  29. Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. (2017) Compressed sensing for body MRI. Journal of Magnetic Resonance Imaging 45:966–987. https://doi.org/10.1002/jmri.25547

    Article  PubMed  Google Scholar 

  30. Yoon S, Shim YS, Park SH, Sung J, Nickel MD, Kim YJ, et al. (2023) Hepatobiliary phase imaging in cirrhotic patients using compressed sensing and controlled aliasing in parallel imaging results in higher acceleration. Eur Radiol. https://doi.org/10.1007/s00330-023-10226-w

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nam JG, Lee JM, Lee SM, Kang H-J, Lee ES, Hur BY, et al. (2019) High Acceleration Three-Dimensional T1-Weighted Dual Echo Dixon Hepatobiliary Phase Imaging Using Compressed Sensing-Sensitivity Encoding: Comparison of Image Quality and Solid Lesion Detectability with the Standard T1-Weighted Sequence. Korean J Radiol 20:438–448. https://doi.org/10.3348/kjr.2018.0310

    Article  PubMed  PubMed Central  Google Scholar 

  32. Choi MH, Kim B, Han D, Lee YJ. (2022) Compressed sensing for breath-hold high-resolution hepatobiliary phase imaging: image noise, artifact, biliary anatomy evaluation, and focal lesion detection in comparison with parallel imaging. Abdominal Radiology 47:133–142. https://doi.org/10.1007/s00261-021-03290-7

    Article  PubMed  Google Scholar 

  33. Jaspan ON, Fleysher R, Lipton ML. (2015) Compressed sensing MRI: a review of the clinical literature. The British Journal of Radiology 88:20150487. https://doi.org/10.1259/bjr.20150487

    Article  PubMed  PubMed Central  Google Scholar 

  34. Akasaka T, Fujimoto K, Yamamoto T, Okada T, Fushumi Y, Yamamoto A, et al. (2016) Optimization of Regularization Parameters in Compressed Sensing of Magnetic Resonance Angiography: Can Statistical Image Metrics Mimic Radiologists’ Perception? PLOS ONE 11:e0146548. https://doi.org/10.1371/journal.pone.0146548

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sandino CM, Cheng JY, Chen F, Mardani M, Pauly JM, Vasanawala SS. (2020) Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks. IEEE Signal Process Mag 37:111–127. https://doi.org/10.1109/msp.2019.2950433

    Article  PubMed  PubMed Central  Google Scholar 

  36. Song JS, Kim SH, Kuehn B, Paek MY. (2020) Optimized Breath-Hold Compressed-Sensing 3D MR Cholangiopancreatography at 3T: Image Quality Analysis and Clinical Feasibility Assessment. Diagnostics 10:376. https://doi.org/10.3390/diagnostics10060376

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zhu L, Xue H, Sun Z, Qian T, Weiland E, Kuehn B, et al. (2018) Modified breath-hold compressed-sensing 3D MR cholangiopancreatography with a small field-of-view and high resolution acquisition: Clinical feasibility in biliary and pancreatic disorders. Journal of Magnetic Resonance Imaging 48:1389–1399. https://doi.org/10.1002/jmri.26049

    Article  PubMed  Google Scholar 

  38. Yoon JH, Lee SM, Kang H-J, Weiland E, Raithel E, Son Y, et al. (2017) Clinical Feasibility of 3-Dimensional Magnetic Resonance Cholangiopancreatography Using Compressed Sensing: Comparison of Image Quality and Diagnostic Performance. Investigative radiology 52:612–619. https://doi.org/10.1097/rli.0000000000000380

    Article  PubMed  Google Scholar 

  39. Wang W, Yang J, Liu J, Li W, Zhao K, Xue K, et al. (2022) Three-dimensional static-fluid MR urography with gradient- and spin-echo (GRASE) at 3.0T: comparison of image quality and diagnostic performance with respiratory-triggered fast spin-echo (FSE). Abdominal Radiology 47:1828–1839. https://doi.org/10.1007/s00261-022-03418-3

    Article  PubMed  Google Scholar 

  40. Park SH, Yoon JH, Park JY, Shim YS, Lee SM, Choi SJ, et al. (2023) Performance of free-breathing dynamic T1-weighted sequences in patients at risk of develo** motion artifacts undergoing gadoxetic acid-enhanced liver MRI. Eur Radiol 33:4378–4388. https://doi.org/10.1007/s00330-022-09336-8

    Article  CAS  PubMed  Google Scholar 

  41. Gassenmaier S, Küstner T, Nickel D, Herrmann J, Hoffmann R, Almansour H, et al. (2021) Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present? 11:2181.

  42. Obuchowski NA, Subhas N, Schoenhagen P. (2014) Testing for interchangeability of imaging tests. Academic radiology 21:1483–1489. https://doi.org/10.1016/j.acra.2014.07.004

    Article  PubMed  Google Scholar 

  43. Johnson PM, Lin DJ, Zbontar J, Zitnick CL, Sriram A, Muckley M, et al. (2023) Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology 307:e220425. https://doi.org/10.1148/radiol.220425

    Article  PubMed  Google Scholar 

  44. Herrmann J, Gassenmaier S, Nickel D, Arberet S, Afat S, Lingg A, et al. (2021) Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold. Investigative radiology 56:313–319. https://doi.org/10.1097/rli.0000000000000743

    Article  CAS  PubMed  Google Scholar 

  45. Herrmann J, Wessling D, Nickel D, Arberet S, Almansour H, Afat C, et al. (2023) Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T. Academic radiology 30:93–102. https://doi.org/10.1016/j.acra.2022.03.018

    Article  PubMed  Google Scholar 

  46. Wary P, Hossu G, Ambarki K, Nickel D, Arberet S, Oster J, et al. (2023) Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study. European Radiology 33:6817–6827. https://doi.org/10.1007/s00330-023-09693-y

    Article  CAS  PubMed  Google Scholar 

  47. Shanbhogue K, Tong A, Smereka P, Nickel D, Arberet S, Anthopolos R, et al. (2021) Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence. Eur Radiol 31:8447–8457. https://doi.org/10.1007/s00330-021-08008-3

    Article  CAS  PubMed  Google Scholar 

  48. Matsumoto S, Tsuboyama T, Onishi H, Fukui H, Honda T, Wakayama T, et al. (2023) Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance. Investigative radiology. https://doi.org/10.1097/rli.0000000000001047

  49. Kim B, Lee CM, Jang JK, Kim J, Lim SB, Kim AY. (2023) Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response. Abdominal radiology (New York) 48:201–210. https://doi.org/10.1007/s00261-022-03701-3

    Article  PubMed  Google Scholar 

  50. Afat S, Herrmann J, Almansour H, Benkert T, Weiland E, Hölldobler T, et al. (2023) Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction. Diagnostic and interventional imaging 104:178–184. https://doi.org/10.1016/j.diii.2022.11.002

    Article  PubMed  Google Scholar 

  51. Zerunian M, Pucciarelli F, Caruso D, Polici M, Masci B, Guido G, et al. (2022) Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation. La Radiologia medica 127:1098–1105. https://doi.org/10.1007/s11547-022-01539-9

    Article  PubMed  PubMed Central  Google Scholar 

  52. Chen Q, Fang S, Yuchen Y, Li R, Deng R, Chen Y, et al. (2023) Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value. European journal of radiology 168:111149. https://doi.org/10.1016/j.ejrad.2023.111149

    Article  PubMed  Google Scholar 

  53. Kim DH, Kim B, Lee HS, Benkert T, Kim H, Choi JI, et al. (2023) Deep Learning-Accelerated Liver Diffusion-Weighted Imaging: Intraindividual Comparison and Additional Phantom Study of Free-Breathing and Respiratory-Triggering Acquisitions. Investigative radiology 58:782–790. https://doi.org/10.1097/rli.0000000000000988

    Article  CAS  PubMed  Google Scholar 

  54. Bae SH, Hwang J, Hong SS, Lee EJ, Jeong J, Benkert T, et al. (2022) Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging. European journal of radiology 154:110428. https://doi.org/10.1016/j.ejrad.2022.110428

    Article  PubMed  Google Scholar 

  55. Almansour H, Gassenmaier S, Nickel D, Kannengiesser S, Afat S, Weiss J, et al. (2021) Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity. Investigative radiology 56:509–516. https://doi.org/10.1097/rli.0000000000000769

    Article  PubMed  Google Scholar 

  56. Wessling D, Herrmann J, Afat S, Nickel D, Almansour H, Keller G, et al. (2022) Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging. Diagnostics (Basel, Switzerland) 12. https://doi.org/10.3390/diagnostics12102370

  57. Almansour H, Herrmann J, Gassenmaier S, Lingg A, Nickel MD, Kannengiesser S, et al. (2023) Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity. Academic radiology 30:863–872. https://doi.org/10.1016/j.acra.2022.06.003

    Article  PubMed  Google Scholar 

  58. Zhang Y, Peng W, **ao Y, Ming Y, Ma K, Hu S, et al. (2023) Rapid 3D breath-hold MR cholangiopancreatography using deep learning–constrained compressed sensing reconstruction. European Radiology 33:2500–2509. https://doi.org/10.1007/s00330-022-09227-y

    Article  PubMed  Google Scholar 

  59. Reeder SB, Cruite I, Hamilton G, Sirlin CB. (2011) Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. Journal of Magnetic Resonance Imaging 34:729–749. https://doi.org/10.1002/jmri.22580

    Article  PubMed  Google Scholar 

  60. Yokoo T, Serai SD, Pirasteh A, Bashir MR, Hamilton G, Hernando D, et al. (2018) Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis. Radiology 286:486–498. https://doi.org/10.1148/radiol.2017170550

    Article  PubMed  Google Scholar 

  61. Reeder SB, Sirlin CB. (2010) Quantification of liver fat with magnetic resonance imaging. Magnetic resonance imaging clinics of North America 18:337–357, ix. https://doi.org/10.1016/j.mric.2010.08.013

  62. Park S, Kwon JH, Kim SY, Kang JH, Chung JI, Jang JK, et al. (2022) Cutoff Values for Diagnosing Hepatic Steatosis Using Contemporary MRI-Proton Density Fat Fraction Measuring Methods. Korean J Radiol 23:1260–1268. https://doi.org/10.3348/kjr.2022.0334

    Article  PubMed  PubMed Central  Google Scholar 

  63. Szczepaniak LS, Nurenberg P, Leonard D, Browning JD, Reingold JS, Grundy S, et al. (2005) Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. 288:E462-E468. https://doi.org/10.1152/ajpendo.00064.2004

  64. Kühn JP, Hernando D, Muñoz del Rio A, Evert M, Kannengiesser S, Völzke H, et al. (2012) Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results. Radiology 265:133–142. https://doi.org/10.1148/radiol.12112520

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kim H, Choi J-I, Lee H-S. (2022) Friend or Foe: How to Suppress and Measure Fat During Abdominal Resonance Imaging? Korean J Abdom Radiol 6:22–36. https://doi.org/10.52668/kjar.2022.00143

    Article  Google Scholar 

  66. Park J, Lee JM, Lee G, Jeon SK, Joo I. (2022) Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques. Korean J Radiol 23:13–29. https://doi.org/10.3348/kjr.2021.0112

    Article  PubMed  PubMed Central  Google Scholar 

  67. Sellers R. (2016) MR LiverLab. MAGNETOM Flash 66: 39–43. https://cdn0.scrvt.com/39b415fb07de4d9656c7b516d8e2d907/1800000003537376/b2961f8cb1ea/Magnetom-Flash-66_How-I-do-it-MR-LiverLab_Sellers_1800000003537376.pdf. Accessed 28 Feb 2024

  68. Song S, Kim H, Choi J-I, Kim DH, Kim B, Lee H, et al. (2023) Validity of an automated screening Dixon technique for quantifying hepatic steatosis in living liver donors. Abdominal Radiology. https://doi.org/10.1007/s00261-023-04009-6

    Article  PubMed  Google Scholar 

  69. Kim H, Taksali SE, Dufour S, Befroy D, Goodman TR, Petersen KF, et al. (2008) Comparative MR study of hepatic fat quantification using single-voxel proton spectroscopy, two-point dixon and three-point IDEAL. Magnetic Resonance in Medicine 59:521–527. https://doi.org/10.1002/mrm.21561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Zhong X, Nickel MD, Kannengiesser SA, Dale BM, Kiefer B, Bashir MR. (2014) Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med 72:1353–1365. https://doi.org/10.1002/mrm.25054

    Article  PubMed  Google Scholar 

  71. Labranche R, Gilbert G, Cerny M, Vu K-N, Soulières D, Olivié D, et al. (2018) Liver Iron Quantification with MR Imaging: A Primer for Radiologists. 38:392–412. https://doi.org/10.1148/rg.2018170079

  72. Ramm GA, Ruddell RG. (2005) Hepatotoxicity of Iron Overload: Mechanisms of Iron-Induced Hepatic Fibrogenesis. Semin Liver Dis 25:433–449. https://doi.org/10.1055/s-2005-923315

    Article  CAS  PubMed  Google Scholar 

  73. Pietrangelo A. (2016) Iron and the liver. 36:116–123. https://doi.org/10.1111/liv.13020

  74. Reeder SB, Yokoo T, França M, Hernando D, Alberich-Bayarri Á, Alústiza JM, et al. (2023) Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR. 307:e221856. https://doi.org/10.1148/radiol.221856

  75. Wood JC, Enriquez C, Ghugre N, Tyzka JM, Carson S, Nelson MD, et al. (2005) MRI R2 and R2* map** accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients. Blood 106:1460–1465. https://doi.org/10.1182/blood-2004-10-3982

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Hankins JS, McCarville MB, Loeffler RB, Smeltzer MP, Onciu M, Hoffer FA, et al. (2009) R2* magnetic resonance imaging of the liver in patients with iron overload. Blood 113:4853–4855. https://doi.org/10.1182/blood-2008-12-191643

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Garbowski MW, Carpenter J-P, Smith G, Roughton M, Alam MH, He T, et al. (2014) Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan. Journal of Cardiovascular Magnetic Resonance 16:40. https://doi.org/10.1186/1532-429X-16-40

    Article  PubMed  PubMed Central  Google Scholar 

  78. Henninger B, Zoller H, Rauch S, Finkenstedt A, Schocke M, Jaschke W, et al. (2015) R2* relaxometry for the quantification of hepatic iron overload: biopsy-based calibration and comparison with the literature. RoFo: Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin 187:472–479. https://doi.org/10.1055/s-0034-1399318

    Article  CAS  PubMed  Google Scholar 

  79. Kannengiesser S. (2016) Iron quantification with LiverLab. MAGNETOM Flash 66:44–46. https://cdn0.scrvt.com/39b415fb07de4d9656c7b516d8e2d907/1800000003537375/763f370f9ee4/Magnetom-Flash-66_Iron-quantification-with-LiverLab_Kannengiesser_1800000003537375.pdf. Accessed 28 Feb 2024.

  80. Trout AT, Serai S, Mahley AD, Wang H, Zhang Y, Zhang B, et al. (2016) Liver Stiffness Measurements with MR Elastography: Agreement and Repeatability across Imaging Systems, Field Strengths, and Pulse Sequences. 281:793–804. https://doi.org/10.1148/radiol.2016160209

  81. Ferraioli G, Wong VW, Castera L, Berzigotti A, Sporea I, Dietrich CF, et al. (2018) Liver Ultrasound Elastography: An Update to the World Federation for Ultrasound in Medicine and Biology Guidelines and Recommendations. Ultrasound in medicine & biology 44:2419–2440. https://doi.org/10.1016/j.ultrasmedbio.2018.07.008

    Article  Google Scholar 

  82. Afdhal NH. (2012) Fibroscan (transient elastography) for the measurement of liver fibrosis. Gastroenterology & hepatology 8:605–607.

    Google Scholar 

  83. Guglielmo FF, Venkatesh SK, Mitchell DG. (2019) Liver MR Elastography Technique and Image Interpretation: Pearls and Pitfalls. 39:1983–2002. https://doi.org/10.1148/rg.2019190034

  84. Wagner M, Besa C, Bou Ayache J, Yasar TK, Bane O, Fung M, et al. (2016) Magnetic Resonance Elastography of the Liver: Qualitative and Quantitative Comparison of Gradient Echo and Spin Echo Echoplanar Imaging Sequences. Investigative radiology 51:575–581. https://doi.org/10.1097/rli.0000000000000269

    Article  PubMed  PubMed Central  Google Scholar 

  85. Kim DW, Kim SY, Yoon HM, Kim KW, Byun JH. (2020) Comparison of technical failure of MR elastography for measuring liver stiffness between gradient-recalled echo and spin-echo echo-planar imaging: A systematic review and meta-analysis. Journal of magnetic resonance imaging: JMRI 51:1086–1102. https://doi.org/10.1002/jmri.26918

    Article  PubMed  Google Scholar 

  86. Singh S, Venkatesh SK, Wang Z, Miller FH, Motosugi U, Low RN, et al. (2015) Diagnostic performance of magnetic resonance elastography in staging liver fibrosis: a systematic review and meta-analysis of individual participant data. Clinical gastroenterology and hepatology: the official clinical practice journal of the American Gastroenterological Association 13:440–451.e446. https://doi.org/10.1016/j.cgh.2014.09.046

    Article  PubMed  Google Scholar 

  87. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, et al. (2013) Magnetic resonance fingerprinting. Nature 495:187–192. https://doi.org/10.1038/nature11971

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Poorman ME, Martin MN, Ma D, McGivney DF, Gulani V, Griswold MA, et al. (2020) Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations. Journal of Magnetic Resonance Imaging 51:675–692. https://doi.org/10.1002/jmri.26836

    Article  PubMed  Google Scholar 

  89. Hong T, Han D, Kim DH. (2019) Simultaneous estimation of PD, T(1), T(2), T(2)(*), and ∆B(0) using magnetic resonance fingerprinting with background gradient compensation. Magn Reson Med 81:2614–2623. https://doi.org/10.1002/mrm.27556

    Article  PubMed  Google Scholar 

  90. Ma D, Pierre EY, Jiang Y, Schluchter MD, Setsompop K, Gulani V, et al. (2016) Music-based magnetic resonance fingerprinting to improve patient comfort during MRI examinations. Magn Reson Med 75:2303–2314. https://doi.org/10.1002/mrm.25818

    Article  PubMed  Google Scholar 

  91. Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. (2023) Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Investigative radiology 58:561–577. https://doi.org/10.1097/rli.0000000000000975

    Article  PubMed  Google Scholar 

  92. Chen Y, Jiang Y, Pahwa S, Ma D, Lu L, Twieg MD, et al. (2016) MR Fingerprinting for Rapid Quantitative Abdominal Imaging. Radiology 279:278–286. https://doi.org/10.1148/radiol.2016152037

    Article  PubMed  Google Scholar 

  93. Jaubert O, Arrieta C, Cruz G, Bustin A, Schneider T, Georgiopoulos G, et al. (2020) Multi-parametric liver tissue characterization using MR fingerprinting: Simultaneous T(1), T(2), T(2) *, and fat fraction map**. Magn Reson Med 84:2625–2635. https://doi.org/10.1002/mrm.28311

    Article  CAS  PubMed  Google Scholar 

  94. Han D, Choi MH, Lee YJ, Kim DH. (2021) Feasibility of Novel Three-Dimensional Magnetic Resonance Fingerprinting of the Prostate Gland: Phantom and Clinical Studies. Korean J Radiol 22:1332–1340. https://doi.org/10.3348/kjr.2020.1362

    Article  PubMed  PubMed Central  Google Scholar 

  95. Yu AC, Badve C, Ponsky LE, Pahwa S, Dastmalchian S, Rogers M, et al. (2017) Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer. Radiology 283:729–738. https://doi.org/10.1148/radiol.2017161599

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank JaeKon Sung, Nickel, Marcel Dominik, Joonsung Lee, and Sang-Young Kim for support provided throughout this project.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to So Hyun Park.

Ethics declarations

Conflict of interest

Munyoung Paek and Dongyeob Han are employees of Siemens Healthineers Ltd. Other authors have no potential conflicts of interest to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, Y., Yoon, S., Paek, M. et al. Advanced MRI techniques in abdominal imaging. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04369-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00261-024-04369-7

Keywords

Navigation