## Quantification

Analysis of DSC-MRI data is based on the assumption that the contrast agent remains within the vascular space throughout the examination, acting as a blood pool marker. This assumption is untrue except in the brain where there is no contrast medium leakage due to the blood-brain barrier. The application of DSC-MRI was, therefore, initially limited to studies of normal brain, although modifications of the technique have subsequently allowed its use in enhancing tissues (see below).

The conventional approach to calculating blood flow uses the area under the contrast concentration curve as an estimate of blood volume within the pixel (BV), and the width of the contrast bolus as an estimate of the mean transit time (MTT).

Average R* Curve and Gamma Variale

Average R* Curve and Gamma Variale

Figure 4 Model fitting of T2*-weighted data and parametric map formation. T2* signal intensity data from Figure 2 (tumor periphery) is converted into R2* (1/T2*) and then fitted with a gamma variate function. Parametric maps representing blood flow kinetics (rBF, rBV, MTT) are derived on a pixel-by-pixel basis. The computed values of rBV, rBF and MTT for this region of interest are 509, 21.3 arbitrary units, and 24 seconds. Abbreviations: rBV, relative blood volume; rBF, relative blood flow; MTT, mean transit time. (See color insert for Fig. 4B.)

MTT is the average time taken by the contrast agent to pass through the tissue being studied (Fig. 4) (13,14,19). Blood flow (BF) can be calculated by using the central volume theorem equation (BF = BV/MTT). The initial calculation of local contrast concentration from the observed signal change is straightforward as contrast concentration is linearly related to the T2 rate changes (AR2), which can be calculated using the relationship

where S(0) is the base line signal intensity, S(t) is the pixel intensity at time t and TE is the echo time. This allows the transformation of signal intensity time course data to changing R2.

The most robust parameter which can be extracted reliably from first pass techniques is BV, which is obtained from the integral of the data time series during the first pass of the contrast agent (20).

rCBV

DR2(t)dt

where t0 is the time of first arrival of contrast and te is the time at which AR2 returns to baseline values. The MTT is then estimated from the width of the curve at half the maximum height [full width at half maximum (FWHM)].

In addition to the flow related parameters described above, it is also possible to calculate time to contrast medium arrival into a tissue (70), or more commonly, the time to peak (TTP) concentration. Additionally, an appreciation of the spatial distribution of tissue perfusion can be obtained by simple subtraction images taken at the nadir point (maximal signal attenuation). This easily obtained image has been strongly correlated with relative blood flow and volume in tumors (compare Fig. 2 with Fig. 4 and Fig. 3 with Fig. 5) (34,35). Subtraction analysis should only be done if there is a linear relationship between rBV and rBF; that is, when MTT is in a narrow range (Figs. 4 and 5). The correlation between the maximum signal intensity drop and rBV/rBF appears good in untreated tumors, but this relationship does

Figure 5 Parametric DCE-MRI images of an invasive ductal cancer of the breast. This is the same tumor illustrated in Figures 3, 6, and 8. Parametric images of rBV, rBF, MTT are shown. The graph shows that there is a linear correlation between blood volume and flow on a pixel level (the gradient of this line is the MTT; rBF = rBV/MTT). Abbreviations: rBV, relative blood volume; rBF, relative blood flow; MTT, mean transit time. (See color insert.)

Figure 5 Parametric DCE-MRI images of an invasive ductal cancer of the breast. This is the same tumor illustrated in Figures 3, 6, and 8. Parametric images of rBV, rBF, MTT are shown. The graph shows that there is a linear correlation between blood volume and flow on a pixel level (the gradient of this line is the MTT; rBF = rBV/MTT). Abbreviations: rBV, relative blood volume; rBF, relative blood flow; MTT, mean transit time. (See color insert.)

not appear to be sustained following therapy (21). An additional parameter that can be derived from DSC-MRI data is the tortuosity index, which is the difference between the total time series integral and the integral of the gamma variate derived from the first pass (see below) (22). The tortuosity index reflects the abnormal retention of contrast material in the tumor vasculature. The tortuosity index can only be derived for brain tumors because there is no or little loss of compartmentalization of contrast medium bolus during the first pass. Absolute quantification of DSC-MRI parameters can be obtained by measuring the changing concentration of contrast agent in the feeding vessel, and in this way, quantified perfusion parameters in normal brain and of low grade gliomas have been obtained (23,24). Absolute quantification is not currently possible for evaluation of visceral tissues and tumors because of a number of limitations that are discussed below.

## Post a comment