# Area-under-curve (AUC) in PET

Most analysis methods of PET data require calculation of integral over time (“area-under-curve”) of the input function, for instance in FUR and Patlak plot; in other cases, including compartmental models the integration of plasma or blood curve is less obvious but still done by the analysis tools. Integration of dynamic PET data is needed in Logan plot, SUV, and SUVR calculations. In addition, AUC from time zero to infinity needs to be estimated in dosimetry and pharmacokinetics.

The AUC of plasma and blood time-activity curves (PTAC and BTAC, respectively) can be calculated by dot-to-dot integration (“trapezoidal rule”), when separate blood samples have been collected manually; each curve value then represents the radioactivity concentration at the time when blood sample was withdrawn (Fig. 1):

Analysis software calculates the integrals similarly. Integrals can also be computed using
command-line program
interpol with option `-i`

.

Since the blood samples in this example are withdrawn relatively infrequently, errors will be introduced if the measured curve does not well represent the true kinetics of the input function. Below, the true input concentration curve and AUC are plotted with the same data as in the previous figure, to show that large error in AUC may result from too sparse blood sampling (Fig. 2):

Therefore, it is important that blood sampling is frequent enough when the concentration is rapidly changing. In bolus infusion PET studies, the plasma/blood peak region is the most crucial part (Fig. 3):

With the command-line program interpol user can optionally select the method how to estimate the initial AUC. Program inpstart can be used in scripts to roughly check that the peak of input curve was not missed, but it must not be used to replace visual verification of the data quality.

One solution is to measure blood TAC using automatic blood sampling system (ABSS), although it has its own problems. In theory, fitting a mathematical function to the sparse input data may help in integration and interpolation; but since the measured data is noisy, and functions are only empirical representations of typical tracer kinetics after successful tracer administration, fitting would just increase uncertainty in the analysis process.

## AUC from time frame data

Tissue time-activity curves (TTACs) and image derived input curves, based on PET measurements, and blood time-activity curves (BTACs) collected using ABSS are collected as mean concentrations during specified time frames. The events (counts) have to be collected over the certain time “frame” to achieve acceptable count statistics, and the number of measured events are then divided by the time frame length to obtain the count rates (counts/s), which are further calibrated to radioactivity concentrations (Bq/mL) based on the measured volume and calibration coefficients. If time frames are sufficiently short, we can assume that the average tracer concentration during the time frame represents the instantaneous concentration at frame middle time point (“midframe approximation”, see Buchert et al., 2003). If concentration changes markedly during the time frame, then the midframe approximation is not valid (Figs 4a and 4b).

To obtain AUC during the time frame, the radioactivity concentration can be simply multiplied by the frame length, and the AUC obtained this way is not biased at the frame end times even with long the frame lengths (Figs 4a and 4b), except for the small error introduced by decay correction which assumes that concentration does not change during the frame. If integral needs to be assessed between the frame start and end times, then some assumptions must be made about the concentration changes during the time frame.

The fact that AUC can be correctly calculated from PET data with long frames can be utilized in PET scan protocols for Patlak and Logan plots; only AUC is needed from the early time range for these graphical analysis methods, and therefore the first image time frames can be long.

Analysis software calculates the integrals based on image time frames, and also regional TTACs may preserve frame start and end times. Integrals, considering the frames lengths, can also be computed using command-line programs dftinteg and imginteg.

## AUC from zero to infinity

The *AUC* from 0 to infinite time, *AUC _{0-∞}*, is needed in
calculation of dosimetry and plasma pharmacokinetics for PET
radioligands. For the sampling duration the

*AUC*can be determined by the trapezoidal rule, but since the last measured concentration is not zero, stopping AUC calculation at that time point would underestimate the

*AUC*. Therefore, the rest of the curve must be extrapolated with exponential function. In practise, line is fitted to the plot of the natural logarithm of tracer concentration against time. This can be done e.g. using program paucinf, or with Excel (Zhang et al., 2010). The slope of the linear part of the plot,

_{0-∞}*A*, is negative and equals

*-k*; the slope and the y axis intercept of the fitted line (

_{el}*B*) can be used to calculate the integral from the last sample time (

*t*) to infinity:

_{LS}, which is then added to the *AUC* calculated from the sampled time range.

### Example

Example dosimetry data, stored in PMOD-formatted text file `kanit.tac`

:

time[hour] kani1 kani2 0,002 1,843998941 12,48854572 0,006 26,57880893 27,15690636 0,010 27,81422174 27,2508763 0,015 24,95289268 30,11531703 0,021 24,6088121 27,46352825 0,029 23,94164827 26,56452008 0,038 26,05074671 28,27240406 0,046 27,18322752 24,62507584 0,058 26,82812168 27,74892528 0,075 24,13405129 30,03561905 0,125 23,84561008 25,51318302 0,208 23,35385837 26,92998525 0,292 22,64704041 26,42876143 0,375 22,64042787 26,72444256 0,458 22,88441149 25,14929462 0,542 22,99261657 25,82539875 0,625 22,94944039 25,73525504 0,750 21,53232992 25,28724687 0,917 20,87775579 25,03024888 24,000 14,519465 17,0535419 48,000 14,52988259 12,33580858 144,000 5,167555956 4,510780064 216,000 3,432414852 2,703595935 288,000 1,335165334 1,105938208 360,000 0,649894738 0,500921454 432,000 0,31633773 0,226886368 504,000 0,153978104 0,102765461 576,000 0,074949189 0,046546384 648,000 0,036481687 0,021082626 720,000 0,017757543 0,009549123 792,000 0,008643524 0,004325161 864,000 0,004207255 0,00195903 936,000 0,002047891 0,000887319 984,000 0,001267201 0,000523326 1056,00 0,000616813 0,000237034

is analyzed with paucinf, saving results in HTML table format for easy importing into Excel (Fig. 5a), and plotting the line fits with ln-transformed data (Fig. 5b):

paucinf -svg=kanit.svg kanit.tac kanit.html

If you are allergic to command line, you can write the above command in to text file, and save it
with extension `.bat`

(in Windows), and then double-click the .bat file icon.

By default, paucinf determines automatically the best time range for the linear fitting. The time range can be optionally refined.

This approach only works with curves approaching zero, which is the case in dosimetry calculations, because the activity data is not corrected for physical decay. In pharmacokinetic calculations decay corrected activity concentrations are used; although concentrations eventually would approach zero, during the time scope of PET scans the concentrations often reach an apparently steady level well above zero; extrapolation with line fitting to ln-transformed curves does not work in that case, but at least two-exponential function is required, and extrapolation and especially AUC calculation will be prone to large errors.

## See also:

- Plasma pharmacokinetics
- Input function
- Simulation of PET time frames
- Patlak plot and long time frames
- Fitting TTACs

## References

Buchert R, van den Hoff J, Mester J. Accurate determination of metabolic rates from dynamic
positron emission tomography data with very-low temporal resolution.
*J Comput Assist Tomography* 2003; 27(4): 597-605. doi:
10.1097/00004728-200307000-00026.

Häggström I, Axelsson J, Schmidtlein CR, Karlsson M, Garpebring A, Johansson L, Sörensen J,
Larson A. A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in
pharmacokinetic parameters from dynamic PET. *J Nucl Med Technol.* 2015; 43: 53-60.
doi: 10.2967/jnmt.114.141754.

Kolthammer JA, Muzic RF. Optimized dynamic framing for PET-based myocardial blood flow estimation.
*Phys Med Biol.* 2013; 58: 5783-5801.
doi: 10.1088/0031-9155/58/16/5783.

Li X, Feng D. Towards the reduction of dynamic image data in positron emission tomography studies.
*Comput Meth Progr Biomed.* 1997; 53: 71-80.
doi: 10.1016/S0169-2607(97)01812-9.

Mazoyer BM, Huesman RH, Budinger TF, Knittel BL. Dynamic PET data analysis.
*J Comput Assist Tomogr.* 1986; 10(4): 645-653. doi:
10.1097/00004728-198607000-00020.

Wallstén E, Axelsson J, Karlsson M, Riklund K, Larsson A. A study of dynamic PET
frame-binning on the reference Logan binding potential. *IEEE TRPMS* 2017; 1(2): 128-135.
doi: 10.1109/TNS.2016.2639560.

Zhang Y, Huo M, Zhou J, Xie S. PKSolver: An add-in program for pharmacokinetic and
pharmacodynamic data analysis in Microsoft Excel.
*Comput Methods Programs Biomed.* 2010; 99: 306-314.
doi: 10.1016/j.cmpb.2010.01.007.

Tags: AUC, TTAC, PTAC, Frame times, FUR, Simulation, Extrapolation

Created at: 2018-01-23

Updated at: 2018-08-21

Written by: Vesa Oikonen