Comparison of six different diffusion weighted MRI models in pancreatic cancer patients

Oliver J. Gurney-Champion^{1,2}, Remy Klaassen^{3}, Martijn Froeling^{1,4}, Jaap Stoker^{1}, Johanna W. Wilmink^{3,5}, Arjan Bel^{2}, Hanneke W.M. van Laarhoven^{3}, and Aart J. Nederveen^{1}

Currently, there are many models available to describe diffusion weighted MRI (DWI) data signal intensity as a function of diffusion weighting (b-values). Among these models are the: classical mono-exponential model (mono-exp), bi-exponential intra-voxel incoherent motion (IVIM)-model, tri-exponential model (tri-exp), stretched-exponent model (stretched-exp), Gaussian-model (Gaussian spread of D-values) and kurtosis-model. Often, the mono-exp and IVIM-models are used, as they are most easy to interpret. However, other models may provide parameters that are either more precise or more sensitive to detect tissue abnormalities.

Therefore, in this research, we evaluated how the abovementioned models perform in pancreatic cancer patients considering the repeatability of the model parameters as well as their potential to differentiate between healthy and tumorous tissue.

We acquired 2D multi-slice diffusion weighted (DW) EPI images in nine patients with either histologically or cytologically proven pancreatic cancer (three females, mean age 69, range 56-77) on a 3T Philips (Ingenia) scanner, using navigator based respiratory triggering. Images were acquired at b=0,10,20,30,40,50,75,100,150,250,400 and 600s/mm^{2}. All patients were scanned three times during two separate sessions (1-8 days apart) to assess intra-session and

All DW-images were denoised using a Rician adaptive non-local means filter^{1} and registered group wise^{2} using a 4D non-rigid b-spline algorithm based on mutual information, in Elastix^{3}. All six models mentioned above were fitted to the data, using the Levenberg-Marquardt least squares fitting algorithm in Matlab. From this, maps were created for the various model parameters: (apparent) diffusion coefficient (ADC; D), perfusion coefficient(s) (f,f_{1} and f_{2}), pseudo-diffusion coefficient (D*), exponential stretch (α), standard deviation of the ^{1} and D*^{2} were fixed to 0.014 and 0.093 mm^{2}s^{-1} (based on healthy pancreatic data in 16 volunteers, data not shown).

For the bi-exponential IVIM-model, we used three fitting
algorithms: Levenberg-Marquardt least squares (IVIM-Free, as used for all other
fits), Levenberg-Marquardt least squares while fixing D* to 50×10^{-3} mm^{2}s^{-1} (IVIM-Fixed)
and the adaptive threshold algorithm^{4} (IVIM-Adaptive).

Per patient, we created two single-slice regions of
interest (ROIs), containing either healthy pancreatic tissue or pancreatic
tumor tissue. The ROIs were drawn on an ADC-map generated from b=100 and 600
s/mm^{2} under the guidance of gadolinium contrast-enhanced Dixon-reconstructed
images. Per patient, we calculated the mean value of the voxel-wise fits within
the ROIs for all fit parameters.

Per fitting parameter, we used a paired t-test to test whether the parameter values from the tumorous ROI were significantly different from the values in healthy tissue (p<0.05). In addition, we tested the repeatability of each fit parameter by calculating the inter- and intra-session within-subject coefficient of variation (CV) from the tumor ROI.

For the mono-

Each model had one fit parameter that was significantly different between tumorous and healthy tissue (Table 1, Figure 2). For the IVIM-model fits, the perfusion fraction was the only parameter that differed significantly.

In general, parameters that showed

We cannot indicate a preference for a specific model based on both repeatability and sensitivity, as the model-parameter with the highest repeatability had poorer sensitivity compared to other model-parameters and vice versa. When high sensitivity is desired, one should use the f_{2} from the tri-

Parameters from multi-parametric models showed complimentary information. Therefore, multi-parametric models may be preferred above the mono-exponential model. However, this characteristic was not yet fully exploited in the current analysis.

From the tested IVIM fit methods, there was no clear preference considering repeatability and difference in healthy and tumorous tissue.

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^{3}S. Klein et al. IEEE Trans. Med.
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^{4}M.C. Wurnig et al. MRM **74**,
1414–1422 (2015).

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

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