Simulation of PET time frames

Time frames

The conventional PET data analysis requires instantaneous values of tracer concentration in the tissue at each time point. However, a dynamic PET study consists of series of integral measurements over time ”frames”. Since in bolus studies the tracer concentration changes during the frame, instantaneous tracer concentrations can not be derived from integrated PET data at any given data point.

Usually it is assumed that the average tracer concentration during a frame represents the instantaneous concentration at frame middle time point (“midframe approximation”, see Buchert et al., 2003). This may be appropriate if time frames are relatively short. Otherwise, a time before frame midpoint time can be used (Parker et al., 1978), or midpoint interpolation should be avoided (Mazoyer et al., 1986).

Temporal resolution can be increased without sacrificing count-statistics by reconstructing images with overlapping frames (Territo et al., 2016).


Program simframe can be used to simulate the effects of PET time frames for regional TACs. Optionally it can simulate the radioactive decay during the time frame. It can be used to simulate frames for input data as well, in case of simulating image-derived input function.

See also:


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.

Feng D, Li X, Siu W-C. Optimal sampling schedule design for positron emission tomography data acquisition. Control Eng Practice 1997; 5(12): 1759-1766. doi: 10.1016/S0967-0661(97)10032-6.

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.

Lee BC, Moody JB, Weinberg RL, Corbett JR, Ficaro EP, Murthy VL. Optimization of temporal sampling for 82rubidium PET myocardial blood flow quantification. J Nucl Cardiol. 2017; 24(5): 1517-1529. doi: 10.1007/s12350-017-0899-7.

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.

Parker JA, Beller GA, Hoop B, Holman BL, Smith TW. Assessment of regional myocardial blood flow and regional fractional oxygen extraction in dogs, using 15O-water and 15O-hemoglobin. Circ Res 1978; 42(4): 511-518. doi: 10.1161/01.RES.42.4.511.

Raylman RR, Caraher JM, Hutchins GD. Sampling requirements for dynamic cardiac PET studies using image-derived input functions. J Nucl Med. 1993; 34(3): 440-447.

Territo PR, Riley AA, McCarthy BP, Hutchins GD. Measurement of cardiovascular function using a novel view-sharing PET reconstruction method and tracer kinetic analysis. EJNMMI Physics 2016; 3: 24. doi: 10.1186/s40658-016-0161-4.

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.

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Updated at: 2019-01-02
Created at: 2010-09-20
Written by: Vesa Oikonen