IVIM in the Body
Eric Sigmund1

1Radiology, NYU Langone Medical Center, New York, NY, United States

Synopsis

Intravoxel incoherent motion (IVIM), which decomposes diffusion-weighted MRI signals in to microcirculation and microstructural components, has seen tremendous application throughout the body. This presentation will review the major trends, findings, and challenges of this surge of activity.

Highlights

  • Intravoxel incoherent motion (IVIM) MRI has found a tremendous surge in application in organs and pathologies in which microcirculation is a significant contributor to water transport.
  • High perfusion and tubular flow in the kidney gives rise to strong IVIM signals that relate to renal function and contribute to diffusion anisotropy.
  • Highly vascularized liver tissue shows IVIM behavior that has been connected with cirrhosis. ·
  • Skeletal muscle IVIM signals have been observed, particularly in response to exercise where blood volume, flow, and myofiber architecture are modulated by exertion.
  • Given the importance of separating cellularity and angiogenesis, cancer is perhaps the leading application of IVIM throughout the body.
  • While many single-site studies have shown clinical potential for IVIM, optimization of acquisition, analysis, and multi-site standardization schemes remains an ongoing area of research.

Introduction

Intravoxel incoherent motion (IVIM) MRI is an extension of conventional diffusion-weighted imaging (DWI) with perhaps the longest track record and a correspondingly wide range of applications. While it is a two-compartment model of water diffusion of which many varieties exist, the ‘IVIM’ connotation involves specifically an effort to separate microcirculation (a.k.a. perfusion, microvascularity) from hindered or restricted Brownian motion through particular choice of sufficient weightings (b-values) to estimate both. One of the first in vivo DWI models (1) that is currently in the midst of a dramatic revival for new organs and pathologies (2), the IVIM approach fits a biexponential decay model to the signal b-value dependence. The biexponential analysis allows measurement of the perfusion volume fraction (fp or f), and separation of the tissue diffusivity (Dt or D) in the parenchyma from pseudodiffusivity (Dp or D*) in the microvasculature. Historically, this approach emerged through a combination of available hardware and comparison with competing image modalities (e.g. CT perfusion), and was predominantly applied for neuroimaging. Progressively, technological improvements such as RF coils for extra-cranial imaging, stronger diffusion gradients, higher field strengths, and improved image processing pipelines ‘revived’ the IVIM approach to several applications to which it is perfectly suited. Specifically, many organs (kidney, liver, pancreas, muscle), as well as many types of cancer, are well described by separating microcirculation from microstructure, with corresponding diagnostic benefit. This presentation will aim to summarize these types of efforts; while not necessarily a comprehensive review, it will indicate the current scope of IVIM in the body, including the most common trends and challenges. The rapidly growing use of IVIM in the body has been recognized in several recent review articles in the body (3,4) , in liver (5), and in the breast (6).

Acquisition and Analysis

The mathematical challenges of multi-exponential fitting are well known, but their particular form in IVIM has seen significant attention. Given frequent minority perfusion fraction, a ‘brute force’ simultaneous estimation of all parameters with conventional least-squares fitting tends to be challenging, especially at the voxel level. A common (though sometimes controversial) approximation (called segmented, multi-step, or curve-stripping) is to fit the upper b-values, where the signal from microcirculation fraction is assumed extinguished, to a monoexpoential model followed by full fits with constrained values for remaining parameters. Proponents see it as a compromise of accuracy and precision that limits variance and allows discovery of useful contrast; opponents argue the approximation too strict and not always justified. Adaptive approaches have been shown that determine the threshold b-value for segmentation on a voxelwise basis (7), increasing per-organ flexibility. Alternative curve-fitting algorithms are finding increasing use such as Bayesian modeling (8,9) (which estimates parameter distributions rather than single values) , and spatially constrained fusion bootstrap solvers (10). While some numerical or spatial priors can be required in these methods, they can produce results with reduced variance, particularly for the microcirculation parameters, than least squares analysis, and have begun to be incorporated into commercial software (11,12).

Significant efforts aim to maximize the practical utility of IVIM on the acquisition side. Optimization of higher order models for the diffusion signal beyond single ADC quantification has been performed (breast (13), liver (14,15), kidney(16), prostate (17,18) , multi-organ (19)); many of these studies find that ‘clustered’ sampling patterns around key b-values (minimally 4 given the 4 unknowns of the IVIM model) outperform more regular sampling with regard to precision and accuracy. An important figure of merit in these optimizations is the repeatability (i.e. reproducibility, test/retest, interscan variance), which is typically good for tissue diffusivity, moderate for perfusion fraction, and poor for pseudodiffusivity (14,20-24). These features are context-dependent and reproducibility of more broad acquisitions is more difficult to manage than targeted ones. Repeated acquisitions in patients are also not always feasible. But the benchmark of reproducible imaging remains an important challenge for advanced imaging approaches such as IVIM.

Kidney

As one of the most highly perfused organs, the kidney was one of the first to demonstrate IVIM signatures (25), and the effect was recognized in meta-analyses as a major source of variance in clinical renal imaging studies employing the monoexponential ADC model (26,27). Since then, vascular and tubular flow have been observed to play key roles in the diffusion contrast in many IVIM studies (24,28-30), and to connect with markers of renal function. IVIM employing cardiac-gating (31) shows maximal cortical perfusion fraction at peak systole. The correlation of monoexponential apparent diffusion coefficient (ADC) with glomerular filtration rate (GFR) (32) has been suggested in some IVIM studies (30,33,34) to originate in part from the perfusion fraction (fp) and pseudodiffusivity (Dp). IVIM analysis showed both structural and microcirculation changes with physiologic challenges of hydration and furosemide administration (24). Recent studies are considering models beyond two-compartment IVIM. A tri-exponential analysis (35) proposes to resolve vascular flow, urine flow, and microstructure by their diffusivities, each with their own spatial pattern. Another ‘generalized’ model (GIVIM) adopts a distribution of pseudodiffusion coefficients and a singular tissue diffusivity and has been applied to both liver (36) and kidney (37).

Another feature of renal tissue is diffusion anisotropy, particularly in the oriented tubules/ducts of the medulla, as measured by diffusion tensor imaging (DTI). Renal medulla was first demonstrated to show DTI anisotropy by Ries et.al. (38) and in a range of following studies (39-42). It is intuitive and likely that microscopic flow contributes to medullary anisotropy, as indirectly suggested by DTI studies showing elevated anisotropy when lower b-values were employed (39). More directly, IVIM medullary pseudodiffusivity (Dp) has shown a comparable anisotropy to the structural tissue diffusivity (Dt) in a combined IVIM/DTI scheme (43) considering the projection of the former onto the orientation of the latter. Another approach employs an intravoxel oriented flow (IVOF) model (44) incorporating an apparent flow fraction tensor to capture the microcirculation and microstructural anisotropy in medullary tissue. Pathologically, IVIM methods have revealed sensitivity to several types of disease. Pseudodiffusivity, and to a lesser extent tissue diffusivity, was observed to progressively decline with lower GFR (30). IVIM metrics have been measured in partially nephrectomized and contralateral kidneys before and after surgery, reflecting compensatory changes (33). Kidney transplant status has also been interrogated with advanced DWI methods, including IVIM (26,28,29,45).

Liver

he liver is another highly vascularized organ for which IVIM analysis is a natural fit. A landmark study by Luciani et.al. (46) garnered significant attention and suggested that cirrhotic tissue was distinguished more by perfusion deficiency than diffusion restriction; ensuing IVIM studies (e.g. (47,48)) indicated both aspects are affected. A significant surge of IVIM applications in the liver has occurred, including technical acquisition aspects (triggering/breath holding, b-value sampling, relaxation time effects, field strength dependence) and a range of pathologies (cirrhosis, fatty liver, fibrosis)(49-56). The state of this research was recently summarized in a comprehensive review of 59 articles by Li et.al. (57) including normative values, protocol and processing variations, and pathologies (fibrosis and liver tumors). They conclude that (a) reasonable ranges for true diffusion and perfusion fraction can be defined, while pseudodiffusion remains variable between studies; (b) trends exist for differentiation fibrotic grades or tumor subtypes but significant group overlap persists; (c) further technical advances are warranted, primarily for breathing motion correction and SNR improvement.

Muscle

While the baseline blood volume of skeletal muscle is low, the IVIM signature has been long recognized in muscle, in part because of the microvascular changes with exercise (58). The transient increases in blood volume, blood flow, and myofiber architecture in challenged muscles can be captured by IVIM, as seen in studies of the calf (58), shoulder (59), and back muscles (60), the latter of which employed a dynamic acquisition. Also, since the microvasculature in skeletal muscle is often aligned with the myofibers, similar anisotropy has been observed in the pseudodiffusion and microstructural components of the signal in an intravoxel partially coherent motion (IVPCM) model (61). Perfusion effects have also been recognized as relevant for DTI or DKI acquisitions in skeletal muscle (62). Regarding pathology, IVIM has been used in pilot studies of polymyositis and dermatomyositis (63), where perfusion fraction and tissue diffusion showed discrimination of inflamed and fat infiltrated muscles. Many more opportunities for IVIM in skeletal muscle pathologies remain to be explored.

Cancer

Hypercellularity in tumors underpins much of ADC contrast. Angiogenesis, or the growth of dysfunctional neovasculature, is another common feature of aggressiveness. Thus, the IVIM model has a natural applicability to resolving these two features separately and preventing their confusion. Correspondingly, there is an extensive literature on IVIM in cancer in the body, such as in liver (64-67), pancreas (68), prostate (69-72), kidney (73-75), and breast (76-89). While this literature is too large to summarize here, most find enhanced lesion perfusion fraction over normal tissue as a useful blood volume measure; the variability of the pseudodiffusivity is typically higher between studies and sites, but still is observed to show significant differentiation of tumor types or correlation with other clinical scores in some cases. Some studies (77,82) indicated advantages of combining parameters for improved diagnostic accuracy. Another common hallmark of aggressive cancer is spatial heterogeneity, and IVIM contrast in tumors is no exception. Histogram analysis is increasingly employed for more complete description of IVIM contrast in tumors (11,82,90-92). It has also been shown that the strength of the IVIM signature, and appropriateness of the model, spatially varies within tumors, so that the diagnostic performance of the IVIM metrics depends crucially on the subsampling approach of lesions (79,81,82). It is to be expected the processing workflow for malignancy detection and treatment response monitoring will require context-specific adaptation.

Alternative and hybrid IVIM models

Another advanced DWI technique (the subject of other reviews) captures non-Gaussian diffusion behavior at high b-values representing the ‘kurtosis’ of the propagator (diffusion kurtosis imaging, DKI). The resulting microstructural tissue description is an improved characterization, especially in densely packed tumor cellularity, but this feature should not be confused hypervascularity (also upregulated in many tumors). Correspondingly, several studies have combined sampling at low, intermediate, and high b-values to determine the parameters of a combined IVIM and DKI model (78,93,94). While a detailed comparison is beyond the scope of this review, it should be noted that many models are employed to characterize the microcirculation / microstructure mixture, such as stretched exponential (SE) (23,81,95-98), and statistical forms(28,99). In short, it should be noted that the explicit compartment specificity of the IVIM model can in some cases come at too high a cost in random variance if the source data quality is not managed.

Translation

Despite the widespread use of conventional ADC in single sites, it is only recently that concerted efforts have been made to generalize its application to multi-site studies supporting large scale clinical trials. The ingredients for success in such efforts (standardization, quality control, common phantoms, multi-reader studies) tend to involve different priorities that in contrast discovery, but they are vital to widespread adoption. The roadmap established by consortia (Quantitative Imaging Biomarkers Alliance (QIBA), Quantitative Imaging Network (QIN)) is primed for adoption by advanced quantitative imaging techniques like IVIM. Standardized phantoms, multi-site studies, and quality control are very worthwhile goals in understanding and enabling its best application in individual diseases.

Conclusions

IVIM is finding increasing application in organs and pathologies throughout the body, with a corresponding increase in technique variations, optimizations, and evidence level. Alongside this innovation stream, the time is also ripe to commit effort to migration and standardization schemes for IVIM—guided by experiences with other MR biomarkers—to keep potential for broad clinical impact in view.

Acknowledgements

No acknowledgement found.

References

1. Lebihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Lavaljeantet M. Separation of Diffusion and Perfusion in Intravoxel Incoherent Motion Mr Imaging. Radiology 1988;168(2):497-505.

2. Iima M, Le Bihan D. Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future. Radiology 2016;278(1):13-32.

3. Koh DM, Collins DJ, Orton MR. Intravoxel Incoherent Motion in Body Diffusion-Weighted MRI: Reality and Challenges. American Journal of Roentgenology 2011;196(6):1351-1361.

4. Taouli B, Beer AJ, Chenevert T, Collins D, Lehman C, Matos C, Padhani AR, Rosenkrantz AB, Shukla-Dave A, Sigmund E, Tanenbaum L, Thoeny H, Thomassin-Naggara I, Barbieri S, Corcuera-Solano I, Orton M, Partridge SC, Koh DM. Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop. J Magn Reson Imaging 2016;44(3):521-540.

5. Li YT, Cercueil J-P, Yuan J, Chen W, Loffroy R, Wáng YXJ. Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation. Quantitative Imaging in Medicine and Surgery 2017;7(1):59-78.

6. Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 2017;45(2):337-355.

7. Wurnig MC, Donati OF, Ulbrich E, Filli L, Kenkel D, Thoeny HC, Boss A. Systematic analysis of the intravoxel incoherent motion threshold separating perfusion and diffusion effects: Proposal of a standardized algorithm. Magn Reson Med 2015;74(5):1414-1422.

8. Neil JJ, Bretthorst GL. On the use of Bayesian probability theory for analysis of exponential decay data: an example taken from intravoxel incoherent motion experiments. Magn Reson Med 1993;29(5):642-647.

9. Orton MR, Collins DJ, Koh DM, Leach MO. Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling. Magn Reson Med 2014;71(1):411-420.

10. Freiman M, Perez-Rossello JM, Callahan MJ, Voss SD, Ecklund K, Mulkern RV, Warfield SK. Reliable estimation of incoherent motion parametric maps from diffusion-weighted MRI using fusion bootstrap moves. Med Image Anal 2013.

11. Lee YJ, Kim SH, Kang BJ, Kang YJ, Yoo H, Yoo J, Lee J, Son YH, Grimm R. Intravoxel incoherent motion (IVIM)-derived parameters in diffusion-weighted MRI: Associations with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 2016. 12. Chaibi Y, Campana Tremblay S, Bucciarelli B. The Diffusion Saga. Olea Imagein : Innovation for Life 2016 November 2016.

13. Cho GY, Moy L, Zhang JL, Baete S, Lattanzi R, Moccaldi M, Babb JS, Kim S, Sodickson DK, Sigmund EE. Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer. Magn Reson Med 2014.

14. Dyvorne H, Jajamovich G, Kakite S, Kuehn B, Taouli B. Intravoxel incoherent motion diffusion imaging of the liver: optimal b-value subsampling and impact on parameter precision and reproducibility. Eur J Radiol 2014;83(12):2109-2113.

15. Dyvorne HA, Galea N, Nevers T, Fiel MI, Carpenter D, Wong E, Orton M, de Oliveira A, Feiweier T, Vachon ML, Babb JS, Taouli B. Diffusion-weighted imaging of the liver with multiple b values: effect of diffusion gradient polarity and breathing acquisition on image quality and intravoxel incoherent motion parameters--a pilot study. Radiology 2013;266(3):920-929.

16. Zhang L, Sigmund EE, Rusinek H, Chandarana H, Storey P, Chen Q, Lee VS. Optimization of b-value sampling for diffusion-weighted imaging of the kidney. Magnetic Resonance in Medicine 2012.

17. Jambor I, Merisaari H, Aronen HJ, Jarvinen J, Saunavaara J, Kauko T, Borra R, Pesola M. Optimization of b-value distribution for biexponential diffusion-weighted MR imaging of normal prostate. Journal of magnetic resonance imaging : JMRI 2013.

18. Merisaari H, Jambor I. Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm : Simulation and repeatability study. Magn Reson Med 2014.

19. Lemke A, Stieltjes B, Schad LR, Laun FB. Toward an optimal distribution of b values for intravoxel incoherent motion imaging. Magn Reson Imaging 2011;29(6):766-776.

20. Kakite S, Dyvorne H, Besa C, Cooper N, Facciuto M, Donnerhack C, Taouli B. Hepatocellular carcinoma: short-term reproducibility of apparent diffusion coefficient and intravoxel incoherent motion parameters at 3.0T. J Magn Reson Imaging 2015;41(1):149-156.

21. Andreou A, Koh DM, Collins DJ, Blackledge M, Wallace T, Leach MO, Orton MR. Measurement reproducibility of perfusion fraction and pseudodiffusion coefficient derived by intravoxel incoherent motion diffusion-weighted MR imaging in normal liver and metastases. Eur Radiol 2013;23(2):428-434.

22. Lee Y, Lee SS, Kim N, Kim E, Kim YJ, Yun SC, Kuhn B, Kim IS, Park SH, Kim SY, Lee MG. Intravoxel incoherent motion diffusion-weighted MR imaging of the liver: effect of triggering methods on regional variability and measurement repeatability of quantitative parameters. Radiology 2015;274(2):405-415.

23. Jerome NP, Miyazaki K, Collins DJ, Orton MR, d'Arcy JA, Wallace T, Moreno L, Pearson AD, Marshall LV, Carceller F, Leach MO, Zacharoulis S, Koh DM. Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort. Eur Radiol 2017;27(1):345-353.

24. Sigmund EE, Vivier P-H, Sui D, Lamparello NA, Tantillo K, Mikheev A, Rusinek H, Babb JS, Storey P, Lee VS, Chandarana H. Intravoxel Incoherent Motion and Diffusion-Tensor Imaging in Renal Tissue under Hydration and Furosemide Flow Challenges. Radiology 2012;263(3):758-769.

25. Muller MF, Prasad PV, Edelman RR. Can the IVIM model be used for renal perfusion imaging? European Journal of Radiology 1998;26(3):297-303.

26. Thoeny HC, Zumstein D, Simon-Zoula S, Eisenberger U, De Keyzer F, Hofmann L, Vock P, Boesch C, Frey FJ, Vermathen P. Functional evaluation of transplanted kidneys with diffusion-weighted and BOLD MR imaging: Initial experience. Radiology 2006;241(3):812-821.

27. Zhang JL, Sigmund EE, Chandarana H, Rusinek H, Chen Q, Vivier P-H, Taouli B, Lee VS. Variability of Renal Apparent Diffusion Coefficients: Limitations of the Monoexponential Model for Diffusion Quantification. Radiology 2010;254(3):783-792.

28. Blondin D, Lanzman RS, Klasen J, Scherer A, Miese F, Kropil P, Wittsack HJ. Diffusion-attenuated MRI signal of renal allografts: comparison of two different statistical models. AJR American journal of roentgenology 2011;196(6):W701-705.

29. Eisenberger U, Thoeny HC, Binser T, Gugger M, Frey FJ, Boesch C, Vermathen P. Evaluation of renal allograft function early after transplantation with diffusion-weighted MR imaging. Eur Radiol 2010;20(6):1374-1383.

30. Ichikawa S, Motosugi U, Ichikawa T, Sano K, Morisaka H, Araki T. Intravoxel incoherent motion imaging of the kidney: alterations in diffusion and perfusion in patients with renal dysfunction. Magnetic resonance imaging 2013;31(3):414-417.

31. Wittsack HJ, Lanzman RS, Quentin M, Kuhlemann J, Klasen J, Pentang G, Riegger C, Antoch G, Blondin D. 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. Investigative radiology 2012;47(4):226-230.

32. Xu Y, Wang X, Jiang X. Relationship between the renal apparent diffusion coefficient and glomerular filtration rate: Preliminary experience. Journal of Magnetic Resonance Imaging 2007;26(3):678-681.

33. Schneider MJ, Dietrich O, Ingrisch M, Helck A, Winter KS, Reiser MF, Staehler M, Casuscelli J, Notohamiprodjo M. Intravoxel Incoherent Motion Magnetic Resonance Imaging in Partially Nephrectomized Kidneys. Invest Radiol 2016;51(5):323-330.

34. Ebrahimi B, Rihal N, Woollard JR, Krier JD, Eirin A, Lerman LO. Assessment of renal artery stenosis using intravoxel incoherent motion diffusion-weighted magnetic resonance imaging analysis. Invest Radiol 2014;49(10):640-646.

35. van Baalen S, Leemans A, Dik P, Lilien MR, Ten Haken B, Froeling M. Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit. J Magn Reson Imaging 2016.

36. Kuai ZX, Liu WY, Zhang YL, Zhu YM. Generalization of intravoxel incoherent motion model by introducing the notion of continuous pseudodiffusion variable. Magn Reson Med 2016;76(5):1594-1603.

37. Ye Q, Chen Z, Zhao Y, Zhang Z, Miao H, Xiao Q, Wang M, Li J. Initial experience of generalized intravoxel incoherent motion imaging and diffusion tensor imaging (GIVIM-DTI) in healthy subjects. J Magn Reson Imaging 2016;44(3):732-738.

38. Ries M, Jones RA, Basseau F, Moonen CTW, Grenier N. Diffusion tensor MRI of the human kidney. Journal of Magnetic Resonance Imaging 2001;14(1):42-49.

39. Notohamiprodjo M, Glaser C, Herrmann KA, Dietrich O, Attenberger UI, Reiser MF, Schoenberg SO, Michaely HJ. Diffusion tensor imaging of the kidney with parallel imaging: Initial clinical experience. Investigative Radiology 2008;43(10):677-685.

40. Kido A, Kataoka M, Yamamoto A, Nakamoto Y, Umeoka S, Koyama T, Maetani Y, Isoda H, Tamai K, Morisawa N, Saga T, Mori S, Togashi K. Diffusion tensor MRI of the kidney at 3.0 and 1.5 Tesla. Acta radiologica 2010;51(9):1059-1063.

41. Cutajar M, Clayden JD, Clark CA, Gordon I. Test-retest reliability and repeatability of renal diffusion tensor MRI in healthy subjects. Eur J Radiol 2011;80(3):e263-268.

42. Gurses B, Kilickesmez O, Tasdelen N, Firat Z, Gurmen N. Diffusion tensor imaging of the kidney at 3 Tesla MRI: normative values and repeatability of measurements in healthy volunteers. Diagn Interv Radiol 2011;17(4):317-322.

43. Notohamiprodjo M, Chandarana H, Mikheev A, Rusinek H, Grinstead J, Feiweier T, Raya JG, Lee VS, Sigmund EE. Combined intravoxel incoherent motion and diffusion tensor imaging of renal diffusion and flow anisotropy. Magnetic Resonance in Medicine 2015.

44. Hilbert F, Bock M, Neubauer H, Veldhoen S, Wech T, Bley TA, Kostler H. An intravoxel oriented flow model for diffusion-weighted imaging of the kidney. NMR Biomed 2016;29(10):1403-1413.

45. DeKeyser F, Thoeny HC. Diffusion-weighted MRI of diffuse renal disease and kidney transplant. In: Taouli B, editor. Extra-Cranial Applications of Diffusion-Weighted MRI: Cambridge University Press; 2011. p 32-45.

46. Luciani A, Vignaud A, Cavet M, Van Nhieu JT, Mallat A, Ruel L, Laurent A, Deux JF, Brugieres P, Rahmouni A. Liver Cirrhosis: Intravoxel Incoherent Motion MR Imaging-Pilot Study. Radiology 2008;249(3):891-899.

47. Patel J, Sigmund EE, Rusinek H, Oei M, Babb JS, Taouli B. Diagnosis of cirrhosis with intravoxel incoherent motion diffusion MRI and dynamic contrast-enhanced MRI alone and in combination: Preliminary experience. Journal of Magnetic Resonance Imaging 2010;31(3):589-600.

48. Hayashi T, Miyati T, Takahashi J, Fukuzawa K, Sakai H, Tano M, Saitoh S. Diffusion analysis with triexponential function in liver cirrhosis. J Magn Reson Imaging 2013;38(1):148-153.

49. Zhang J, Guo Y, Tan X, Zheng Z, He M, Xu J, Mei

50. Zhang B, Liang L, Dong Y, Lian Z, Chen W, Liang C, Zhang S. Intravoxel Incoherent Motion MR Imaging for Staging of Hepatic Fibrosis. PloS one 2016;11(1):e0147789.

51. Parente DB, Paiva FF, Oliveira Neto JA, Machado-Silva L, Figueiredo FA, Lanzoni V, Campos CF, do Brasil PE, Gomes Mde B, Perez Rde M, Rodrigues RS. Intravoxel Incoherent Motion Diffusion Weighted MR Imaging at 3.0 T: Assessment of Steatohepatitis and Fibrosis Compared with Liver Biopsy in Type 2 Diabetic Patients. PloS one 2015;10(5):e0125653.

52. Ichikawa S, Motosugi U, Morisaka H, Sano K, Ichikawa T, Enomoto N, Matsuda M, Fujii H, Onishi H. MRI-based staging of hepatic fibrosis: Comparison of intravoxel incoherent motion diffusion-weighted imaging with magnetic resonance elastography. J Magn Reson Imaging 2015;42(1):204-210.

53. Chung SR, Lee SS, Kim N, Yu ES, Kim E, Kuhn B, Kim IS. Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study. Acta Radiol 2015;56(12):1428-1436.

54. Yoon JH, Lee JM, Baek JH, Shin CI, Kiefer B, Han JK, Choi BI. Evaluation of hepatic fibrosis using intravoxel incoherent motion in diffusion-weighted liver MRI. J Comput Assist Tomogr 2014;38(1):110-116.

55. Lu PX, Huang H, Yuan J, Zhao F, Chen ZY, Zhang Q, Ahuja AT, Zhou BP, Wang YX. Decreases in molecular diffusion, perfusion fraction and perfusion-related diffusion in fibrotic livers: a prospective clinical intravoxel incoherent motion MR imaging study. PloS one 2014;9(12):e113846.

56. Chen C, Wang B, Shi D, Fu F, Zhang J, Wen Z, Zhu S, Xu J, Lin Q, Li J, Dou S. Initial study of biexponential model of intravoxel incoherent motion magnetic resonance imaging in evaluation of the liver fibrosis. Chinese medical journal 2014;127(17):3082-3087.

57. Li YT, Cercueil JP, Yuan J, Chen W, Loffroy R, Wang YX. Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation. Quant Imaging Med Surg 2017;7(1):59-78.

58. Morvan D. In vivo measurement of diffusion and pseudo-diffusion in skeletal muscle at rest and after exercise. Magn Reson Imaging 1995;13(2):193-199.

59. Nguyen A, Ledoux JB, Omoumi P, Becce F, Forget J, Federau C. Application of intravoxel incoherent motion perfusion imaging to shoulder muscles after a lift-off test of varying duration. NMR Biomed 2016;29(1):66-73.

60. Filli L, Boss A, Wurnig MC, Kenkel D, Andreisek G, Guggenberger R. Dynamic intravoxel incoherent motion imaging of skeletal muscle at rest and after exercise. NMR Biomed 2015;28(2):240-246.

61. Karampinos DC, King KF, Sutton BP, Georgiadis JG. Intravoxel partially coherent motion technique: characterization of the anisotropy of skeletal muscle microvasculature. Journal of magnetic resonance imaging : JMRI 2010;31(4):942-953.

62. De Luca A, Bertoldo A, Froeling M. Effects of perfusion on DTI and DKI estimates in the skeletal muscle. Magn Reson Med 2016.

63. Qi J, Olsen NJ, Price RR, Winston JA, Park JH. Diffusion-weighted imaging of inflammatory myopathies: polymyositis and dermatomyositis. Journal of magnetic resonance imaging : JMRI 2008;27(1):212-217.

64. Yoon JH, Lee JM, Yu MH, Kiefer B, Han JK, Choi BI. Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: Comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters. J Magn Reson Imaging 2014;39(2):276-285.

65. Yamada I, Aung W, Himeno Y, Nakagawa T, Shibuya H. Diffusion Coefficients in Abdominal Organs and Hepatic Lesions: Evaluation with Intravoxel Incoherent Motion Echo-planar MR Imaging. Radiology 1999;210(3):617-623.

66. Doblas S, Wagner M, Leitao HS, Daire JL, Sinkus R, Vilgrain V, Van Beers BE. Determination of Malignancy and Characterization of Hepatic Tumor Type With Diffusion-Weighted Magnetic Resonance Imaging: Comparison of Apparent Diffusion Coefficient and Intravoxel Incoherent Motion-Derived Measurements. Invest Radiol 2013.

67. Yoon JH, Lee JM, Yu MH, Kiefer B, Han JK, Choi BI. Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: Comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters. J Magn Reson Imaging 2013.

68. Lemke A, Laun FB, Klauss M, Re TJ, Simon D, Delorme S, Schad LR, Stieltjes B. Differentiation of Pancreas Carcinoma From Healthy Pancreatic Tissue Using Multiple b-Values Comparison of Apparent Diffusion Coefficient and Intravoxel Incoherent Motion Derived Parameters. Investigative Radiology 2009;44(12):769-775.

69. Riches SF, Hawtin K, Charles-Edwards EM, de Souza NM. Diffusion-weighted imaging of the prostate and rectal wall: comparison of biexponential and monoexponential modelled diffusion and associated perfusion coefficients. Nmr in Biomedicine 2009;22(3):318-325.

70. Shinmoto H, Tamura C, Soga S, Shiomi E, Yoshihara N, Kaji T, Mulkern RV. An intravoxel incoherent motion diffusion-weighted imaging study of prostate cancer. AJR Am J Roentgenol 2012;199(4):W496-500.

71. Pang Y, Turkbey B, Bernardo M, Kruecker J, Kadoury S, Merino MJ, Wood BJ, Pinto PA, Choyke PL. Intravoxel incoherent motion MR imaging for prostate cancer: An evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations. Magn Reson Med 2012.

72. Dopfert J, Lemke A, Weidner A, Schad LR. Investigation of prostate cancer using diffusion-weighted intravoxel incoherent motion imaging. Magn Reson Imaging 2011;29(8):1053-1058.

73. Chandarana H, Taouli B, Hecht E, Lee VS, Sigmund EE. Comparison of Biexponential and Monoexponential Model of Diffusion Weighted Imaging in Evaluation of Renal Lesions: Preliminary Experience. Investigative Radiology 2011;46(5):285-291.

74. Chandarana H, Kang SK, Wong S, Rusinek H, Zhang JL, Arizono S, Huang WC, Melamed J, Babb JS, Suan EF, Lee VS, Sigmund EE. Diffusion-weighted intravoxel incoherent motion imaging of renal tumors with histopathologic correlation. Investigative Radiology 2012;47(12):688-696.

75. Rheinheimer S, Stieltjes B, Schneider F, Simon D, Pahernik S, Kauczor HU, Hallscheidt P. Investigation of renal lesions by diffusion-weighted magnetic resonance imaging applying intravoxel incoherent motion-derived parameters--initial experience. European journal of radiology 2012;81(3):e310-316.

76. Sigmund EE, Cho GY, Kim S, Finn M, Moccaldi M, Jensen JH, Sodickson DK, Goldberg JD, Formenti S, Moy L. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magnetic Resonance in Medicine 2011;65(5):1437-1447.

77. Bokacheva L, Kaplan JB, Giri DD, Patil S, Gnanasigamani M, Nyman CG, Deasy JO, Morris EA, Thakur SB. Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J Magn Reson Imaging 2014;40(4):813-823.

78. Iima M, Yano K, Kataoka M, Umehana M, Murata K, Kanao S, Togashi K, Le Bihan D. Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Investigative radiology 2015;50(4):205-211.

79. Suo S, Lin N, Wang H, Zhang L, Wang R, Zhang S, Hua J, Xu J. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer at 3.0 tesla: Comparison of different curve-fitting methods. J Magn Reson Imaging 2015;42(2):362-370.

80. Dijkstra H, Dorrius MD, Wielema M, Jaspers K, Pijnappel RM, Oudkerk M, Sijens PE. Semi-automated quantitative intravoxel incoherent motion analysis and its implementation in breast diffusion-weighted imaging. J Magn Reson Imaging 2015.

81. Panek R, Borri M, Orton M, O'Flynn E, Morgan V, Giles SL, deSouza N, Leach MO, Schmidt MA. Evaluation of diffusion models in breast cancer. Med Phys 2015;42(8):4833-4839.

82. Cho GY, Moy L, Kim SG, Baete SH, Moccaldi M, Babb JS, Sodickson DK, Sigmund EE. Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol 2015.

83. Kim Y, Ko K, Kim D, Min C, Kim SG, Joo J, Park B. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes. The British journal of radiology 2016:20160140.

84. Zhou S, Yi Y, Xu L. Comments on "Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions". Magn Reson Imaging 2017.

85. Ma D, Lu F, Zou X, Zhang H, Li Y, Zhang L, Chen L, Qin D, Wang B. Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions. Magn Reson Imaging 2017;36:175-179.

86. Yuan J, Wong OL, Lo GG, Chan HH, Wong TT, Cheung PS. Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors. Quant Imaging Med Surg 2016;6(4):418-429.

87. Ostenson J, Pujara AC, Mikheev A, Moy L, Kim SG, Melsaether AN, Jhaveri K, Adams S, Faul D, Glielmi C, Geppert C, Feiweier T, Jackson K, Cho GY, Boada FE, Sigmund EE. Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer. Magn Reson Med 2016.

88. Liu C, Wang K, Chan Q, Liu Z, Zhang J, He H, Zhang S, Liang C. Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging. Eur Radiol 2016;26(11):3888-3898.

89. Che S, Zhao X, Ou Y, Li J, Wang M, Wu B, Zhou C. Role of the Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Pre-treatment Prediction and Early Response Monitoring to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer. Medicine 2016;95(4):e2420.

90. Gaing B, Sigmund EE, Huang WC, Babb JS, Parikh NS, Stoffel D, Chandarana H. Subtype Differentiation of Renal Tumors Using Voxel-Based Histogram Analysis of Intravoxel Incoherent Motion Parameters. Investigative radiology 2014.

91. Lemasson B, Galban CJ, Boes JL, Li Y, Zhu Y, Heist KA, Johnson TD, Chenevert TL, Galban S, Rehemtulla A, Ross BD. Diffusion-Weighted MRI as a Biomarker of Tumor Radiation Treatment Response Heterogeneity: A Comparative Study of Whole-Volume Histogram Analysis versus Voxel-Based Functional Diffusion Map Analysis. Transl Oncol 2013;6(5):554-561.

92. Nougaret S, Vargas HA, Lakhman Y, Sudre R, Do RK, Bibeau F, Azria D, Assenat E, Molinari N, Pierredon MA, Rouanet P, Guiu B. Intravoxel Incoherent Motion-derived Histogram Metrics for Assessment of Response after Combined Chemotherapy and Radiation Therapy in Rectal Cancer: Initial Experience and Comparison between Single-Section and Volumetric Analyses. Radiology 2016;280(2):446-454.

93. Pang Y, Turkbey B, Bernardo M, Kruecker J, Kadoury S, Merino MJ, Wood BJ, Pinto PA, Choyke PL. Intravoxel incoherent motion MR imaging for prostate cancer: an evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations. Magn Reson Med 2013;69(2):553-562.

94. Lu Y, Jansen JF, Mazaheri Y, Stambuk HE, Koutcher JA, Shukla-Dave A. Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer. J Magn Reson Imaging 2012;36(5):1088-1096.

95. Mazaheri Y, Afaq A, Rowe DB, Lu Y, Shukla-Dave A, Grover J. Diffusion-weighted magnetic resonance imaging of the prostate: improved robustness with stretched exponential modeling. Journal of computer assisted tomography 2012;36(6):695-703.

96. Lin M, Yu X, Chen Y, Ouyang H, Wu B, Zheng D, Zhou C. Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma. Eur Radiol 2016.

97. Li H, Liang L, Li A, Hu Y, Hu D, Li Z, Kamel IR. Monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma. J Magn Reson Imaging 2016.

98. Hall MG, Bongers A, Sved P, Watson G, Bourne RM. Assessment of non-Gaussian diffusion with singly and doubly stretched biexponential models of diffusion-weighted MRI (DWI) signal attenuation in prostate tissue. NMR Biomed 2015;28(4):486-495.

99. Wittsack HJ, Lanzman RS, Mathys C, Janssen H, Modder U, Blondin D. Statistical evaluation of diffusion-weighted imaging of the human kidney. Magn Reson Med 2010;64(2):616-622.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)