Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: effects on pharmacokinetic parameters and their diagnostic performance

Ahmed Othman¹, Florian Falkner¹, Petros Martirosian¹, Jakob Weiss¹, Stephan Kruck², Robert Grimm³, Konstantin Nikolaou¹, and Mike Notohamiprodjo¹

¹Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany, ²Department of Urology, University Hospital Tübingen, Tübingen, Germany, ³Siemens Healthcare, Siemens Healthcare, Erlangen, Germany

Synopsis

The Choice of arterial input function (AIF) is a potential source of variability in DCE-MRI studies. In clinical practice, it’s not always possible to estimate individual AIFs due to artifacts or difficulties in vessel detection, particularly in transversal slices, which are typically acquired for prostate MRI. Therefore, population averaged AIFs (pAIFs) are often used. In the present study we assessed the effect of different pAIFs on parameter estimates in DCE-MRI of the prostate. We found that choosing various pAIF types causes high variability in pharmacokinetic parameter estimates. Therefore, it is important to keep AIF type selection constant in DCE-MRI studies.

Purpose

To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions.

Materials and methods

66 male patients (age 65.4±10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3T MRI scanner. Two radiologists rated the likelihood of malignancy of detected lesions on multi-parametric MRI using the ACR PI-RADS v2, i.e. applying a 5-point rating scale (1: highly unlikely, 2: unlikely, 3: equivocal, 4: likely, 5: highly likely). Patients were then divided into 2 groups depending on PI-RADS score of the detected lesions (A: PI-RADS ≤3, n=32; B: PI-RADS >3, n=34). In all patients, DCE-MRI was performed using a CDT-VIBE sequence (spatial resolution = 3 mm x 1.2 mm x 1.2 mm, temporal resolution = 5 s, total sampling duration = 4:10 min = 250 s) with body-weight-adapted administration of contrast agent (Gadobutrol, Bayer Healthcare, Berlin, Germany). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (Chi2) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow). The pAIFs were derived from bi-exponential adaptations of the following pAIFs: fast, Fritz-Hansen et al. (1), intermediate, Parker et al. (2), slow, Weinmann et al. (3). The pAIFs used for this analysis are shown in Figs. 1 and 2. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs.

Results

Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p<.001). Ktrans and Kep were significantly higher in group B compared to group A (all p<.001). For Chi2, lowest results (representing highest goodness of fit) were found for intermediate pAIF (Chi2 0.073). Diagnostic accuracies of Ktrans and Kep were high for all 3 AIFs. In contrast, ve yielded remarkably lower diagnostic accuracies for all 3 AIFs. However, diagnostic accuracy did not differ significantly between the different AIFs due to an overlap of the corresponding 95%-CIs for all 3 pharmacokinetic parameters (Fig. 3).

Conclusion

Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. Therefore, it is of great importance to consider this as potential artifact and thus keep AIF type selection constant in DCE-MRI studies.

Acknowledgements

None

References

1. Fritz-Hansen T, Rostrup E, Larsson HB, et al. Measurement of the arterial concentration of Gd-DTPA using MRI: a step toward quantitative perfusion imaging. Magn Reson Med. 1996;36(2):225-31.

2. Parker GJ, Roberts C, Macdonald A, et al. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med. 2006;56(5):993-1000.

3. Weinmann HJ, Laniado M, Mutzel W. Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers. Physiol Chem Phys Med NMR. 1984;16(2):167-72.

Figures

Figure 1: Gadolinium concentration for “fast”, “intermediate” and “slow” pAIF settings of MR Tissue4D (Dose = 0.1 mmol/kg).

Figure 2: Potentially malignant lesion in the left peripheral zone of the prostate (red arrows) presenting with low T2 signal, bulging of the organ capsule, diffusion restriction on ADC map and typical DCE-MRI pattern (early wash-in and wash-out); Lesion was classified as PI-RADS IV.

Figure 3: Effect of pAIF type on diagnostic accuracy of pharmacokinetic parameters.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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