Optimizing injected dose in clinical PET by accurately modeling the counting-rate response functions specific to...

To optimize the injected dose of radiopharmaceutical in PET, one needs to know its relationship to some metric of data quality for individual patient scans, such as noise-equivalent counting rate (NECR). In this paper, we show how one may accurately model the clinical NECR response corresponding to specific patient scans much as if a counting-rate test had been performed on each patient. We apply this technique to patient data and show how it can lead to improved clinical scanning protocols.

True and random coincidence rates expressed as functions of an appropriate measurable system parameter such as the detector single-event rate have functional forms that are largely independent of the object being scanned. Thus, reference true and random response functions may be scaled directly to the specific counting rates measured on a clinical scan, thereby yielding a curve of NECR versus injected dose. We have applied this technique to 2 groups of 163 clinical (18)F-FDG scans each. One of the groups was obtained on a lutetium oxyorthosilicate PET/CT scanner with conventional front-end electronics, and the other was obtained on a lutetium oxyorthosilicate PET/CT scanner with a new digital data processing system (Pico-3D).

At 90%-95% of maximum signal-to-noise ratio (SNR), the mean optimal dose for a 60-min uptake period ranged from 366 to 717 MBq depending on the electronics and randoms processing method. There was only a slight (1 MBq/kg) dependence of optimal dose on patient weight but a larger dependence on position in the body. Pico-3D electronics improved optimal data SNR by 35% for a 70-kg person, but in both cases NECR fell rapidly with increasing weight (1.4%/kg). For an equivalent data SNR, a 120-kg person would have to be scanned 2.3 times longer than a 60-kg person. Over this range of weight, the mean scatter fraction increased by 12% whereas the ratio of mean randoms to trues increased by 48%.

The methodology developed here allows one to directly estimate the optimal dose to inject for specific clinical scans and permits a detailed analysis of the sources of noise in PET data and of their variation with parameters such as patient weight.