SER

Time course data acquired during the injection of a contrast agent may have image intensity variations caused by changes in the magnetic resonance relaxation rate constant T1 in MR exams. This results in positive enhancement.

The time course pixel intensity s_i is expressed as:

s_i = s0 f(T1, t),

A parameter that is used to characterize the time intensity changes is the integral of the area I under the enhancement curve.

The protocol returns the difference between the value of the integral over the image range and the pre-enhancement value.

The function will be background corrected as described above for the Negative Enhancement Integral algorithm, using either a constant base or an interpolated base.

The integral is computed over the enhancement image range (between last pre-enhancement and first post-enhancement image).

The sign parameter must have been set to positive.

Select a constant or an interpolated base as required.

When the positive enhancement integral functional map is displayed in the left function view, the corresponding baseline is shown on the graph view either as a blue segment for the cursor ROI curve or as a red segment for the currently selected user ROI.

The single valued parameter returned by this function is given by:

where S₁ is the signal intensity of the wash–in image (corresponding to the peak of contrast uptake), S_avepre is the

average pre-enhancement signal intensity, and S₂ is the signal intensity of the wash–out image (corresponding to a delayed time point when tissue enhanced significantly and some wash–out has occurred). S_avepre can be defined as the average of user-specified image ranges.

This function is a special case of the generic Ratio (A–B)/(C–D) algorithm, with:

A=S₁

B=N..M (average over pre-enhancement image range)

C=S₂

D=N..M (as B)

Any pixels with (S₂– S_{ave pre}) less than the threshold (defined in the ”advanced settings” of the SER protocol) will appear black on the functional map.

One way to characterize image intensity changes during a dynamic process is to calculate the slope of the time course values at each time course index, i, given by:

slope_i = s_i+1 – s_i.

The single-valued parameter returned by the Maximum Slope of Increase algorithm is simply the maximum value of the slope_i function:

MAX_i=0,N (slope_i).

By analogy, the Maximum Slope of Decrease algorithm returns the minimum value of the slope_i function:

MIN_i=0,N (slope_i).

Maximum slope is computed and displayed only for the images between the last pre–enhancement and first post–enhancement image.

When the functional map computed with either of the maximum slope algorithms is displayed in the left function view, the location of the maximum slope of increase or decrease is shown on the graph view either as a blue segment for the cursor ROI curve or as a red segment for the currently selected user ROI.