OBJECTIVE. The purpose of this study was to assess the impact of a noise reduction technique on image quality, radiation dose, and low-contrast detectability in abdominal CT for obese patients. MATERIALS AND METHODS. A liver phantom with 12 different tumors was designed, and fat rings were added to mimic intermediately sized and large patients. The intermediate and large phantoms were scanned with our standard abdominal CT protocol (image noise level of 15 HU and filtered back projection [FBP]). The large phantom was scanned with five different noise levels (10, 12.5, 15, 17.5, and 20 HU). All datasets for the large phantom were reconstructed with FBP and the noise reduction technique. The image noise and the contrast-to-noise ratio (CNR) were assessed. Tumor detection was independently performed by three radiologists in a blinded fashion. RESULTS. The application of the noise reduction method to the large phantom decreased the measured image noise (range, -14.5% to -37.0%) and increased the CNR (range, 26.7-70.6%) compared with FBP at the same noise level ( p < 0.001). However, noise reduction was unable to improve the sensitivity for tumor detection in the large phantom compared with FBP at the same noise level ( p > 0.05). Applying a noise level of 15 HU, the overall sensitivity for tumor detection in the intermediate and large phantoms with FBP measured 75.5% and 87.7% and the radiation doses measured 42.0 and 23.7 mGy, respectively. CONCLUSION. Although noise reduction significantly improved the quantitative image quality in simulated large patients undergoing abdominal CT compared with FBP, no improvement was observed for low-contrast detectability. © American Roentgen Ray Society.
CITATION STYLE
Schindera, S. T., Odedra, D., Mercer, D., Thipphavong, S., Chou, P., Szucs-Farkas, Z., & Rogalla, P. (2014). Hybrid iterative reconstruction technique for abdominal CT protocols in obese patients: Assessment of image quality, radiation dose, and low-contrast detectability in a phantom. American Journal of Roentgenology, 202(2). https://doi.org/10.2214/AJR.12.10513
Mendeley helps you to discover research relevant for your work.