Objectives The aim of the study was to find the lowest possible tube current and the optimal iterative reconstruction (IR) strength in abdominal imaging. Material and methods Reconstruction software was used to insert noise, simulating the use of a lower tube current. A semi-anthropomorphic abdominal phantom (Quality Assurance in Radiology and Medicine, QSA-543, Moehrendorf, Germany) was used to validate the performance of the ReconCT software (S1 Appendix). Thirty abdominal CT scans performed with a standard protocol (120 kVref, 150 mAsref) scanned at 90 kV, with dedicated contrast media (CM) injection software were selected. There were no other in- or exclusion criteria. The software was used to insert noise as if the scans were performed with 90, 80, 70 and 60% of the full dose. Consequently, the different scans were reconstructed with filtered back projection (FBP) and IR strength 2, 3 and 4. Both objective (e.g. Hounsfield units [HU], signal to noise ratio [SNR] and contrast to noise ratio [CNR]) and subjective image quality were evaluated. In addition, lesion detection was graded by two radiologists in consensus in another 30 scans (identical scan protocol) with various liver lesions, reconstructed with IR 3, 4 and 5. Results A tube current of 60% still led to diagnostic objective image quality (e.g. SNR and CNR) when IR strength 3 or 4 were used. IR strength 4 was preferred for lesion detection. The subjective image quality was rated highest for the scans performed at 90% with IR 4. Conclusion A tube current reduction of 10–40% is possible in case IR 4 is used, leading to the highest image quality (10%) or still diagnostic image quality (40%), shown by a pairwise comparison in the same patients.

ObjectivesThe aims of this study were to develop a proof-of-concept computer algorithm to automatically determine noise, spatial resolution, and contrast-related image quality (IQ) metrics in abdominal portal venous phase computed tomography (CT) imaging and to assess agreement between resulting objective IQ metrics and subjective radiologist IQ ratings.Materials and MethodsAn algorithm was developed to calculate noise, spatial resolution, and contrast IQ parameters. The algorithm was subsequently used on 2 datasets of anthropomorphic phantom CT scans, acquired on 2 different scanners (n = 57 each), and on 1 dataset of patient abdominal CT scans (n = 510). These datasets include a range of high to low IQ: in the phantom dataset, this was achieved through varying scanner settings (tube voltage, tube current, reconstruction algorithm); in the patient dataset, lower IQ images were obtained by reconstructing 30 consecutive portal venous phase scans as if they had been acquired at lower mAs. Five noise, 1 spatial, and 13 contrast parameters were computed for the phantom datasets; for the patient dataset, 5 noise, 1 spatial, and 18 contrast parameters were computed. Subjective IQ rating was done using a 5-point Likert scale: 2 radiologists rated a single phantom dataset each, and another 2 radiologists rated the patient dataset in consensus. General agreement between IQ metrics and subjective IQ scores was assessed using Pearson correlation analysis. Likert scores were grouped into 2 categories, “insufficient” (scores 1–2) and “sufficient” (scores 3–5), and differences in computed IQ metrics between these categories were assessed using the Mann-Whitney U test.ResultsThe algorithm was able to automatically calculate all IQ metrics for 100% of the included scans. Significant correlations with subjective radiologist ratings were found for 4 of 5 noise (R2 range = 0.55–0.70), 1 of 1 spatial resolution (R2 = 0.21 and 0.26), and 10 of 13 contrast (R2 range = 0.11–0.73) parameters in the phantom datasets and for 4 of 5 noise (R2 range = 0.019–0.096), 1 of 1 spatial resolution (R2 = 0.11), and 16 of 18 contrast (R2 range = 0.008–0.116) parameters in the patient dataset. Computed metrics that significantly differed between “insufficient” and “sufficient” categories were 4 of 5 noise, 1 of 1 spatial resolution, 9 and 10 of 13 contrast parameters for phantom the datasets and 3 of 5 noise, 1 of 1 spatial resolution, and 10 of 18 contrast parameters for the patient dataset.ConclusionThe developed algorithm was able to successfully calculate objective noise, spatial resolution, and contrast IQ metrics of both phantom and clinical abdominal CT scans. Furthermore, multiple calculated IQ metrics of all 3 categories were in agreement with subjective radiologist IQ ratings and significantly differed between “insufficient” and “sufficient” IQ scans. These results demonstrate the feasibility and potential of algorithm-determined objective IQ. Such an algorithm should be applicable to any scan and may help in optimization and quality control...