Mareike Alicja Buck^{1} and Matthias Günther^{1,2}

^{1}Fraunhofer MEVIS, Bremen, Germany, ^{2}MR-Imaging and Spectroscopy, Faculty 01 (Physics, Electrical Engineering), University Bremen, Bremen, Germany

This abstract compares quantified perfusion values of the standard model and a new dispersion model based on an AIF using the ASLIF-sequence. For different ATTs, the voxel´s signal was simulated using the dispersion model. The simulations show that the standard model overestimates the signal. This may result from lack of dispersion effects especially in the inflow phase of the labeled bolus. Consequently, the determined perfusion values vary for different ATTS. Thus, using an AIF based on an acquired patient specific reference bolus instead could improve the stability and robustness of quantified perfusion values.

The perfusion phantom QASPER

A model is developed which simulates the ATT dependent shape of the label bolus during the inflow. Using this model, the signal in a voxel was simulated for ATTs between 500ms and 1600ms. The simulated measurements at three inflow times (TI) (2000ms, 3000ms and 4000ms) were then used to determine the quantified perfusion values with the standard model (MatLab, MathWorks, Natick, MA, USA). For the calculation it is assumed that the ATT is known and only the perfusion f needs to be determined. The following simulation parameters were used: f=60 ml/100g/min, T1_b=1500ms, T1_tis=2000ms, lambda=90 ml/100g, M0=500, ATT=500-1600ms, BD=1400ms, alpha=0.9.

The table in Figure 3a shows the fitted quantified perfusion values of the standard model for different ATTs. The determined perfusion values in the range of ATT=700-1300ms are initially overestimated with the standard model but approach the adjusted value of the dispersion model with increasing ATT (Fig. 3b). The observed relative standard deviation is in the range of 12% with a mean value of 6% over all ATTs. Figure 4 presents the fitted signal of the standard model with the quantified perfusion values resulting from the standard model fit compared to the simulated signal of the dispersion model. In all four cases, the comparison shows that the signal is overestimated during the arrival phase of the labeled bolus in the standard model compared to the dispersion model.

For ATT=1300ms the signal of the dispersion model and the standard model have nearly identical parameters. However, at this point the signal of the inflow phase of the standard model is overestimated. This shows that quantification errors may occur by using the standard model. An AIF based on an acquired reference bolus can take dispersion effects into account instead of a boxcar shaped AIF.

The comparison of the two models shows how important it is to consider dispersion effects for a robust quantification.

Using the ASLIF sequence, it is possible to measure a reference bolus directly behind the labeling slice. This reference bolus represents the individual resulting AIF of the labeling. By using this reference bolus with the dispersion model instead of a boxcar shaped AIF, it can be possible to detect and extrapolate dispersion effects, disturbances or deviations in the labeling process. The incorporation of a reference bolus for an ATT dependent AIF could improve the robustness and stability of perfusion quantification and may therefore be an important step towards the clinical application of ASL.

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