Skip to main content

Advertisement

SpringerLink
Log in

Spatial profiling of in vivo diffusion-weighted MRI parameters in the healthy human kidney

  • Research Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge. One input to this process is a whole organ distillation of corticomedullary contrast of diffusion metrics, as has been explored for other renal biomarkers.

Materials and methods

In this work, we probe the spatial dependence of diffusion MRI metrics with concentrically layered segmentation in 11 healthy kidneys at 3 T. The metrics include those from DTI, IVIM, a combined approach titled “REnal Flow and Microstructure AnisotroPy (REFMAP)”, and a multiply encoded model titled “FC-IVIM” providing estimates of fluid velocity and branching length.

Results

Fractional anisotropy decreased from the inner kidney to the outer kidney with the strongest layer correlation in both parenchyma (including cortex and medulla) and medulla with Spearman correlation coefficients and p-values (r, p) of (0.42, <0.001) and (0.37, <0.001), respectively. Also, dynamic parameters derived from the three models significantly decreased with a high correlation from the inner to the outer parenchyma or medulla with (r, p) ranges of (0.46–0.55, <0.001).

Conclusions

These spatial trends might find implications for indirect assessments of kidney physiology and microstructure using diffusion MRI.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Bane O, Seeliger E, Cox E, Stabinska J et al (2023) Renal MRI: from nephron to NMR signal. J Magn Reson Imaging 58:1660–1679

    Article  PubMed  PubMed Central  Google Scholar 

  2. Le Bihan D (2019) What can we see with IVIM MRI? Neuroimage 187:56–67

    Article  PubMed  Google Scholar 

  3. Sigmund EE, Mikheev A, Brinkmann IM, Gilani N et al (2023) Cardiac phase and flow compensation effects on renal flow and microstructure anisotropy MRI in healthy human kidney. J Magn Reson Imaging 58(1):210–220

    Article  PubMed  Google Scholar 

  4. Gilani N (2024) Editorial for “Editorial for “Utility of prostate health index density for biopsy strategy in Biopsy-Naïve patients With PI-RADSv2.1 category 3 lesions”. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29269

    Article  PubMed  Google Scholar 

  5. Pierpaoli C (2010) Artifacts in diffusion MRI. In: DK Jones (ed) Diffusion MRI: theory, methods and applications. Oxford University Press, Oxford, pp 303–318

    Chapter  Google Scholar 

  6. Gilani N, Mikheev A, Brinkmann IM, Basukala D et al (2023) Characterization of motion dependent magnetic field inhomogeneity for DWI in the kidneys. Magn Reson Imaging 100:93–101

    Article  CAS  PubMed  Google Scholar 

  7. Lanzman RS, Ljimani A, Muller-Lutz A, Weller J et al (2019) Assessment of time-resolved renal diffusion parameters over the entire cardiac cycle. Magn Reson Imaging 55:1–6

    Article  PubMed  Google Scholar 

  8. Ito K, Hayashida M, Kanki A, Yamamoto A et al (2018) Alterations in apparent diffusion coefficient values of the kidney during the cardiac cycle: evaluation with ECG-triggered diffusion-weighted MR imaging. Magn Reson Imaging 52:1–8

    Article  PubMed  Google Scholar 

  9. Wittsack HJ, Lanzman RS, Quentin M, Kuhlemann J et al (2012) Temporally resolved electrocardiogram-triggered diffusion-weighted imaging of the human kidney: correlation between intravoxel incoherent motion parameters and renal blood flow at different time points of the cardiac cycle. Invest Radiol 47(4):226–230

    Article  PubMed  Google Scholar 

  10. Milani B, Ledoux JB, Rotzinger DC, Kanemitsu M et al (2019) Image acquisition for intravoxel incoherent motion imaging of kidneys should be triggered at the instant of maximum blood velocity: evidence obtained with simulations and in vivo experiments. Magn Reson Med 81(1):583–593

    Article  PubMed  Google Scholar 

  11. Heusch P, Wittsack HJ, Kropil P, Blondin D et al (2013) Impact of blood flow on diffusion coefficients of the human kidney: a time-resolved ECG-triggered diffusion-tensor imaging (DTI) study at 3T. J Magn Reson Imaging 37(1):233–236

    Article  PubMed  Google Scholar 

  12. Gilani N, Mikheev A, Brinkmann IM, Basukala D et al (2023) The effect of cardiac gating on the repeatability of quantitative renal diffusion MRI. In: Proceedings of the International Society for Magnetic Resonance in Medicine, Toronto, Canada, p 1498

  13. Heptinstall RH (2007) Heptinstall’s pathology of the kidney, vol 1. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  14. Gilani N, Malcolm P, Johnson G (2017) A monte carlo study of restricted diffusion: implications for diffusion MRI of prostate cancer. Magn Reson Med 77(4):1671–1677

    Article  CAS  PubMed  Google Scholar 

  15. Gilani N, Malcolm P, Johnson G (2017) An improved model for prostate diffusion incorporating the results of monte carlo simulations of diffusion in the cellular compartment. NMR Biomed 30(12):e3782

    Article  Google Scholar 

  16. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168(2):497–505

    Article  PubMed  Google Scholar 

  17. Thoeny HC, Keyzer FD (2011) Diffusion-weighted MR Imaging of native and transplanted kidneys. Radiology 259(1):25–38

    Article  PubMed  Google Scholar 

  18. Zhang JL, Sigmund EE, Chandarana H, Rusinek H et al (2010) Variability of renal apparent diffusion coefficients: limitations of the monoexponential model for diffusion quantification. Radiology 254(3):783–792

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ljimani A, Caroli A, Laustsen C, Francis S et al (2020) Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI. MAGMA 33(1):177–195

    Article  CAS  PubMed  Google Scholar 

  20. Caroli A, Schneider M, Friedli I, Ljimani A et al (2018) Diffusion-weighted magnetic resonance imaging to assess diffuse renal pathology: a systematic review and statement paper. Nephrol Dial Transplant 33(2S):ii29–ii40

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ries M, Jones RA, Basseau F, Moonen CT, Grenier N (2001) Diffusion tensor MRI of the human kidney. J Magn Reson Imaging JMRI 14(1):42–49

    Article  CAS  PubMed  Google Scholar 

  22. Periquito JS, Gladytz T, Millward JM, Delgado PR et al (2021) Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system. Quant Imaging Med Surg 11(7):3098–3119

    Article  PubMed  PubMed Central  Google Scholar 

  23. van Baalen S, Leemans A, Dik P, Lilien MR, Ten Haken B, Froeling M (2017) Intravoxel incoherent motion modeling in the kidneys: comparison of mono-, bi-, and triexponential fit. J Magn Reson Imaging 46(1):228–239

    Article  PubMed  Google Scholar 

  24. Notohamiprodjo M, Chandarana H, Mikheev A, Rusinek H et al (2014) Combined intravoxel incoherent motion and diffusion tensor imaging of renal diffusion and flow anisotropy. Magn Reson Med. https://doi.org/10.1002/mrm.25245

    Article  PubMed  Google Scholar 

  25. Hilbert F, Bock M, Neubauer H, Veldhoen S et al (2016) An intravoxel oriented flow model for diffusion-weighted imaging of the kidney. NMR Biomed 29(10):1403–1413

    Article  PubMed  Google Scholar 

  26. Van Phi VD, Becker AS, Ciritsis A, Reiner CS, Boss A (2018) Intravoxel incoherent motion analysis of abdominal organs: application of simultaneous multislice acquisition. Invest Radiol 53(3):179–185

    Article  Google Scholar 

  27. Wetscherek A, Stieltjes B, Laun FB (2015) Flow-compensated intravoxel incoherent motion diffusion imaging. Magn Reson Med 74(2):410–419

    Article  PubMed  Google Scholar 

  28. Stabinska J, Ljimani A, Zollner HJ, Wilken E et al (2021) Spectral diffusion analysis of kidney intravoxel incoherent motion MRI in healthy volunteers and patients with renal pathologies. Magn Reson Med 85(6):3085–3095

    Article  CAS  PubMed  Google Scholar 

  29. Basser PJ, Pierpaoli C (1996) Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson Ser B 111(3):209–219

    Article  CAS  Google Scholar 

  30. Lemberskiy G, Rosenkrantz AB, Veraart J, Taneja SS, Novikov DS, Fieremans E (2017) Time-dependent diffusion in prostate cancer. Invest Radiol 52(7):405–411

    Article  PubMed  Google Scholar 

  31. Gurney-Champion OJ, Rauh SS, Harrington K, Oelfke U, Laun FB, Wetscherek A (2020) Optimal acquisition scheme for flow-compensated intravoxel incoherent motion diffusion-weighted imaging in the abdomen: an accurate and precise clinically feasible protocol. Magn Reson Med 83(3):1003–1015

    Article  PubMed  Google Scholar 

  32. Piskunowicz M, Hofmann L, Zuercher E, Bassi I et al (2015) A new technique with high reproducibility to estimate renal oxygenation using BOLD-MRI in chronic kidney disease. Magn Reson Imaging 33(3):253–261

    Article  PubMed  Google Scholar 

  33. Milani B, Ansaloni A, Sousa-Guimaraes S, Vakilzadeh N et al (2017) Reduction of cortical oxygenation in chronic kidney disease: evidence obtained with a new analysis method of blood oxygenation level-dependent magnetic resonance imaging. Nephrol Dial Transplant 32(12):2097–2105

    CAS  PubMed  Google Scholar 

  34. Li LP, Milani B, Pruijm M, Kohn O et al (2020) Renal BOLD MRI in patients with chronic kidney disease: comparison of the semi-automated twelve layer concentric objects (TLCO) and manual ROI methods. MAGMA 33(1):113–120

    Article  PubMed  Google Scholar 

  35. Zhao K, Li S, Liu Y, Li Q et al (2023) Diagnostic and prognostic performance of renal compartment volume and the apparent diffusion coefficient obtained from magnetic resonance imaging in mild, moderate and severe diabetic kidney disease. Quant Imaging Med Surg 13(6):3973–3987

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sanmiguel Serpa LC, De Visschere P, Speeckaert M, Pullens P (2023) A new method to analyse renal perfusion: a proof of concept. In: Proceedings of the International Society for Magnetic Resonance in Medicine, Toronto, Canada, p 3803

  37. Veraart J, Fieremans E, Novikov DS (2016) Diffusion MRI noise mapping using random matrix theory. Magn Reson Med 76(5):1582–1593

    Article  CAS  PubMed  Google Scholar 

  38. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(1S):S208–S219

    Article  PubMed  Google Scholar 

  39. Führes T, Saake M, Szczepankiewicz F, Bickelhaupt S, Uder M et al (2023) Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs. PLOS ONE 18(10):e0291273. https://doi.org/10.1371/journal.pone.0291273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jones DK, Basser PJ (2004) “Squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted MR data. Magn Reson Med 52(5):979–993

    Article  PubMed  Google Scholar 

  41. Hirsch JG, Schwenk SM, Rossmanith C, Hennerici MG, Gass A (2003) Deviations from the diffusion tensor model as revealed by contour plot visualization using high angular resolution diffusion-weighted imaging (HARDI). MAGMA 16(2):93–102

    Article  PubMed  Google Scholar 

  42. Funck C, Laun FB, Wetscherek A (2018) Characterization of the diffusion coefficient of blood. Magn Reson Med 79(5):2752–2758

    Article  CAS  PubMed  Google Scholar 

  43. Johnson GA, Benveniste H, Black RD, Hedlund LW, Maronpot RR, Smith BR (1993) Histology by magnetic resonance microscopy. Magn Reson Q 9(1):1–30

    CAS  PubMed  Google Scholar 

  44. de Rochefort L, Liu T, Kressler B, Liu J et al (2010) Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med 63(1):194–206

    Article  PubMed  Google Scholar 

  45. Xie L, Bennett KM, Liu C, Johnson GA, Zhang JL, Lee VS (2016) MRI tools for assessment of microstructure and nephron function of the kidney. Am J Physiol Renal Physiol 311(6):F1109–F1124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Morozov D, Parvin N, Charlton JR, Bennett KM (2021) Mapping kidney tubule diameter ex vivo by diffusion MRI. Am J Physiol Renal Physiol 320(5):F934–F946

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Taphorn K, Busse M, Brantl J, Gunther B et al (2022) X-ray stain localization with near-field ptychographic computed tomography. Adv Sci (Weinh) 9(24):e2201723

    Article  PubMed  Google Scholar 

  48. Puelles VG, Combes AN, Bertram JF (2021) Clearly imaging and quantifying the kidney in 3D. Kidney Int 100(4):780–786

    Article  PubMed  Google Scholar 

  49. Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254

    Article  PubMed  Google Scholar 

  50. Afzali M, Pieciak T, Newman S, Garyfallidis E et al (2021) The sensitivity of diffusion MRI to microstructural properties and experimental factors. J Neurosci Methods 347:108951

    Article  PubMed  PubMed Central  Google Scholar 

  51. Liu AL, Mikheev A, Rusinek H, Huang WC et al (2018) REnal flow and microstructure anisotroPy (REFMAP) MRI in normal and peritumoral renal tissue. J Magn Reson Imaging 48(1):188–197

    Article  PubMed  PubMed Central  Google Scholar 

  52. Fioretto P, Sutherland DER, Najafian B, Mauer M (2006) Remodeling of renal interstitial and tubular lesions in pancreas transplant recipients. Kidney Int 69(5):907–912

    Article  CAS  PubMed  Google Scholar 

  53. Kriz W, Napiwotzky P (1979) Structural and functional aspects of the renal interstitium. Contrib Nephrol 16:104–108

    Article  CAS  PubMed  Google Scholar 

  54. Scott LA, Dickie BR, Rawson SD, Coutts G et al (2021) Characterisation of microvessel blood velocity and segment length in the brain using multi-diffusion-time diffusion-weighted MRI. J Cereb Blood Flow Metab 41(8):1939–1953

    Article  CAS  PubMed  Google Scholar 

  55. Gilani N, Hildebrand S, Schueth A, Roebroeck A (2019) Monte Carlo simulation of diffusion MRI in geometries constructed from two-photon microscopy of human cortical grey matter. bioRxiv. https://doi.org/10.1101/626945

  56. Kirilina E, Helbling S, Morawski M, Pine K et al (2020) Superficial white matter imaging: contrast mechanisms and whole-brain in vivo mapping. Sci Adv 6(41):eaaz9281

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ahlgren A, Knutsson L, Wirestam R, Nilsson M et al (2016) Quantification of microcirculatory parameters by joint analysis of flow-compensated and non-flow-compensated intravoxel incoherent motion (IVIM) data. NMR Biomed 29(5):640–649

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fournet G, Li JR, Cerjanic AM, Sutton BP, Ciobanu L, Le Bihan D (2017) A two-pool model to describe the IVIM cerebral perfusion. J Cereb Blood Flow Metab 37(8):2987–3000

    Article  PubMed  Google Scholar 

  59. Kennan RP, Gao JH, Zhong J, Gore JC (1994) A general model of microcirculatory blood flow effects in gradient sensitized MRI. Med Phys 21(4):539–545

    Article  CAS  PubMed  Google Scholar 

  60. Weinstein JR, Anderson S (2010) The aging kidney: physiological changes. Adv Chronic Kidney Dis 17(4):302–307

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institutes of Health (NIH) (R01CA245671), and performed under the rubric of the Center for Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net), an NIBIB National Center for Biomedical Imaging and Bioengineering (NIH P41 EB017183).

Author information

Authors and Affiliations

  1. Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA

    Nima Gilani, Artem Mikheev, Malika Kumbella, James S. Babb, Dibash Basukala, Hersh Chandarana & Eric E. Sigmund

  2. Siemens Healthcare GmbH, Erlangen, Germany

    Inge M. Brinkmann

  3. Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK

    Andreas Wetscherek

  4. MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

    Thomas Benkert

Authors
  1. Nima Gilani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Artem Mikheev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inge M. Brinkmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Malika Kumbella
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. James S. Babb
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dibash Basukala
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andreas Wetscherek
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thomas Benkert
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hersh Chandarana
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Eric E. Sigmund
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Nima Gilani: Methodology, Software, Writing-Original Draft. Artem Mikheev and Andreas Wetscherek: Software, Writing-Review & Editing. Inge M. Brinkmann and Thomas Benkert: Resources, Writing-Review & Editing. Dibash Basukala: Writing-Review & Editing. James S. Babb: Formal analysis. Malika Kumbella: Project administration. Hersh Chandarana: Writing-Review & Editing, Supervision, Funding acquisition. Eric E. Sigmund: Conceptualization, Investigation, Writing-Original Draft, Supervision, Funding acquisition.

Corresponding author

Correspondence to Nima Gilani.

Ethics declarations

Conflict of interest

Co-authors Inge M. Brinkmann, PhD and Thomas Benkert, PhD are employees of SIEMENS Healthineers.

Ethical standards

All enrolled subjects provided written informed consent, and the ethics committee of our hospital approved this prospecrtive study (study number s20-01048).

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (JPG 433 KB)

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

Gilani, N., Mikheev, A., Brinkmann, I.M. et al. Spatial profiling of in vivo diffusion-weighted MRI parameters in the healthy human kidney. Magn Reson Mater Phy (2024). https://doi.org/10.1007/s10334-024-01159-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10334-024-01159-6

Keywords

Search

Navigation