Photon count rates estimated from 1980 clinical CT scans

BACKGROUND: All photon counting detectors have a characteristic count rate over which their performance degrades. Degradation in the clinical setting takes the form of increased noise, reduced material quantification accuracy, and image artifacts. Count rate is a function of patient attenuation, beam filtration, scanner geometry, and X‐ray technique. PURPOSE: To guide protocol and technology development in the photon counting space, knowledge of clinical count rates spanning the complete range of clinical indications and patient sizes is needed. In this paper, we use clinical data to characterize the range of computed tomography (CT) count rates. METHODS: We retrospectively gathered 1980 patient exams spanning the entire body (head/neck/chest/abdomen/extremity) and sampled 36 951 axial image slices. We assigned the tissue labels air/lung/fat/soft tissue/bone to each voxel for each slice using CT number thresholds. We then modeled four different bowtie filters, 70/80/100/120/140 kV spectra, and a range of mA values. We forward‐projected each slice to obtain detector‐incident count rates, using the geometry of a GE Revolution Apex scanner. Our analysis divided the detector into thirds: the central one‐third, one‐third of the detector split into two equal regions adjacent to the central third, and the final one‐third divided equally between the outer detector edges. We report the 99th percentile of counts to mimic the upper limits of count rates making passing through a patient as a function of patient water equivalent diameter. We also report the percentage of patient scans, by body region, over different count rate thresholds for all combinations of bowtie and beam energy. RESULTS: For routine exam types, we recorded count rates of approximately 3.5 × 10(8) counts/mm(2)/s in the torso, extremities, and brain. For neck scans, we observed count rates near 6 × 10(8) counts/mm(2)/s. Our simulations of 1000 mA, appropriately mimicking the mA needs for fast pediatric, fast thoracic, and cardiac scanning, resulted in count rates of over 10 × 10(8) counts/mm(2)/s for the torso, extremities, and brain. At 1000 mA, for the neck region, we observed count rates close to 2 × 10(9) counts/mm(2)/s. Importantly, we saw only a small change in maximum count rate needs over patient size, which we attribute to patient mis‐positioning with respect to the bowtie filters. As expected, combinations of kV and bowtie filter with higher beam energies and wider/less attenuating bowtie fluence profiles lead to higher count rates relative to lower energies. The 99th–50th percentile count rate changed the most for the torso region, with a maximum variation of 3.9 × 10(8) to 1.2 × 10(7) counts/mm(2)/s. The head/neck/extremity regions had less than a 50% change in count rate from the 99th to 50th percentiles. CONCLUSIONS: Our results are the first to use a large patient cohort spanning all body regions to characterize count rates in CT. Our results should be useful in helping researchers understand count rates as a function of body region and mA for various combinations of bowtie filter designs and beam energies. Our results indicate clinical rates >1 × 10(9) counts/mm(2)/s, but they do not predict the image quality impact of using a detector with lower characteristic count rates.