A gamma camera count rate saturation correction method for whole-body planar imaging

Abstract

Whole-body (WB) planar imaging has long been one of the staple methods of dosimetry, and its quantification has been formalized by the MIRD Committee in pamphlet no 16. One of the issues not specifically addressed in the formalism occurs when the count rates reaching the detector are sufficiently high to result in camera count saturation. Camera dead-time effects have been extensively studied, but all of the developed correction methods assume static acquisitions. However, during WB planar (sweep) imaging, a variable amount of imaged activity exists in the detector's field of view as a function of time and therefore the camera saturation is time dependent. A new time-dependent algorithm was developed to correct for dead-time effects during WB planar acquisitions that accounts for relative motion between detector heads and imaged object. Static camera dead-time parameters were acquired by imaging decaying activity in a phantom and obtaining a saturation curve. Using these parameters, an iterative algorithm akin to Newton's method was developed, which takes into account the variable count rate seen by the detector as a function of time. The algorithm was tested on simulated data as well as on a whole-body scan of high activity Samarium-153 in an ellipsoid phantom. A complete set of parameters from unsaturated phantom data necessary for count rate to activity conversion was also obtained, including build-up and attenuation coefficients, in order to convert corrected count rate values to activity. The algorithm proved successful in accounting for motion- and time-dependent saturation effects in both the simulated and measured data and converged to any desired degree of precision. The clearance half-life calculated from the ellipsoid phantom data was calculated to be 45.1 h after dead-time correction and 51.4 h with no correction; the physical decay half-life of Samarium-153 is 46.3 h. Accurate WB planar dosimetry of high activities relies on successfully compensating for camera saturation which takes into account the variable activity in the field of view, i.e. time-dependent dead-time effects. The algorithm presented here accomplishes this task.

Figure 1

The different indices i, j and k from equations (4) and (5) are illustrated against a backdrop of an imaged tumor ROI. Here i is the column number of the imaged object denoted by the solid line. For each i, the index j denotes the y-position of the leading edge of the detector, valid for all different positions where it images the ith column. The detector is indicated by the two dotted lines. For each of those j detector positions, the sum of activities in all the k columns, from j − W + 1 to j, seen by the camera is the activity that contributes to the dead-time effect.

Figure 2

Illustrates the correction algorithm in one dimension, an adaptation of Newton’s method. Here A_t is the true activity and C_m is the measured count rate.

Figure 3

Two-dimensional dosimetry model representing the variables present in equations (13) and (14).

Figure 4

Phantom saturation curve. The phantom consists of 1.0 cm of plexiglass covering 1.2 cm of solution, so the appropriate attenuation modifiers were applied. The triangles are the measured counts per second (cps) values as a function of the decaying activity. The solid line shows the fit using equation (1) and the dotted line shows the theoretically unsaturated linear response. The determined α and β values are the same as used in figure 2, but the range of activities extends further out here.

Figure 5

Iterative saturation correction results for the simulated (computer-generated) data. (a) The original ‘true’ data; (b) the simulated ‘measured’ data. (c) The ratio of ‘measured’ to ‘true’ data. The corrected data are compared with the original ‘true’ data as percent difference in (d).

Figure 6

Phantom data. (a) The counts measured in the phantom using a whole-body sweep with a log scale. The y-axes are the same for both images. (b) The x-axis shows the average count rate per pixel across the y-column of pixels in (a).

Figure 7

First time point phantom corrected count rate results. (a) The ratio of the corrected values to the measured count rates as a function of y-position (the y-axis in figure 6). (b) The difference between the re-simulated count rate and measured count rate as a function of y-position, reflecting the precision of the correction (for a maximum difference of 0.0022%).