Diffusion kurtosis imaging of the liver at 3 Tesla: in vivo comparison to standard diffusion-weighted imaging

Abstract

Background

Functional techniques like diffusion-weighted imaging (DWI) are gaining more and more importance in liver magnetic resonance imaging (MRI). Diffusion kurtosis imaging (DKI) is an advanced technique that might help to overcome current limitations of DWI.

Purpose

To evaluate DKI for the differentiation of hepatic lesions in comparison to conventional DWI at 3 Tesla.

Material and Methods

Fifty-six consecutive patients were examined using a routine abdominal MR protocol at 3 Tesla which included DWI with b-values of 50, 400, 800, and 1000 s/mm². Apparent diffusion coefficient maps were calculated applying a standard mono-exponential fit, while a non-Gaussian kurtosis fit was used to obtain DKI maps. ADC as well as Kurtosis-corrected diffusion (D) values were quantified by region of interest analysis and compared between lesions.

Results

Sixty-eight hepatic lesions (hepatocellular carcinoma [HCC] [n = 25]; hepatic adenoma [n = 4], cysts [n = 18]; hepatic hemangioma [HH] [n = 18]; and focal nodular hyperplasia [n = 3]) were identified. Differentiation of malignant and benign lesions was possible based on both DWI ADC as well as DKI D-values (P values were in the range of 0.04 to < 0.0001).

Conclusion

In vivo abdominal DKI calculated using standard b-values is feasible and enables quantitative differentiation between malignant and benign liver lesions. Assessment of conventional ADC values leads to similar results when using b-values below 1000 s/mm² for DKI calculation.

Keywords

Magnetic resonance imaging (MRI)diffusion-weighted imaging (DWI)diffusion kurtosis imaging (DKI)liver focal liver lesions 3 Tesla

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References

Jensen

Helpern

Ramani

et al.

Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005; 53: 1432–1440.

Jensen

Helpern

. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010; 23: 698–710.

Jensen

Ramani

et al.

Three-dimensional characterization of non-gaussian water diffusion in humans using diffusion kurtosis imaging. NMR Biomed 2006; 19: 236–247.

Jensen

Falangola

et al.

Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction. NMR Biomed 2011; 24: 452–457.

Raab

Hattingen

Franz

et al.

Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 2010; 254: 876–881.

Wang

Lin

et al.

Parkinson disease: diagnostic utility of diffusion kurtosis imaging. Radiology 2011; 261: 210–217.

Van Cauter

Veraart

Sijbers

et al.

Gliomas: diffusion kurtosis MR imaging in grading. Radiology 2012; 263: 492–501.

Rosenkrantz

Sigmund

Johnson

et al.

Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology 2012; 264: 126–135.

Goshima

Kanematsu

Noda

et al.

Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. Am J Roentgenol 2015; 204: W543–549.

10.

Rosenkrantz

Sigmund

Winnick

et al.

Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants. Magn Reson Imaging 2012; 30: 1534–1540.

11.

Kartalis

Manikis

Loizou

et al.

Diffusion-weighted MR imaging of pancreatic cancer: A comparison of mono-exponential, bi-exponential and non-Gaussian kurtosis models. Eur J Radiol Open 2016; 3: 79–85.

12.

Weisser G, Sauter E, Zöllner F, et al. UMMDiffusion: Eine OpenSource Software zur klinischen Evaluation der Kurtosis Bildgebung. Fortschr Röntgenstr, German Congress of Radiology; 2014. 10.1055/s-0034-1372714.

13.

Zöllner

Kaiser

Weisser

et al.

UMMDiffusion: An OsiriX plug-in for ADC and IVIM analysis in clinical routine. Proc Intl Soc Mag Reson Med 2013; 21: 3115–3115.

14.

Rosenkrantz

Padhani

Chenevert

et al.

Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice. J Magn Reson Imaging 2015; 42: 1190–1202.

15.

Glenn

Tabesh

Jensen

. A simple noise correction scheme for diffusional kurtosis imaging. Magn Reson Imaging 2015; 33: 124–133.

16.

Taouli

. Diffusion-weighted MR imaging for liver lesion characterization: a critical look. Radiology 2012; 262: 378–380.

17.

Lemke

Laun

Klauss

et al.

Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Invest Radiol 2009; 44: 769–775.

18.

Lemke

Laun

Simon

et al.

An in vivo verification of the intravoxel incoherent motion effect in diffusion-weighted imaging of the abdomen. Magn Reson Med 2010; 64: 1580–1585.

19.

Cieszanowski

Podgorska

Rosiak

et al.

Gd-EOB-DTPA-enhanced MR imaging of the liver: the effect on T2 relaxation times and apparent diffusion coefficient (ADC). Pol J Radiol 2016; 81: 103–109.

20.

Malzacher

Kalayciyan

Konstandin

et al.

Sodium-23 MRI of whole spine at 3 Tesla using a 5-channel receive-only phased-array and a whole-body transmit resonator. Z Med Phys 2016; 26: 95–100.