PURPOSE: To develop a new automatic exposure control (AEC) technique based on the contrast-to-noise ratio (CNR) and provide constant lesion detectability. METHODS: Lesion detectability is affected by factors such as image noise, lesion contrast, and lesion size. We performed ROC analysis to assess the relationship between the optimum CNR and the lesion diameter at various levels of lesion contrast. We then developed a CNR-based AEC algorithm based on lesion detectability. Using CNR- based AEC algorithm, we performed visual evaluation of low-contrast detectability by 5 radiologists on a low-contrast module of the Catphan phantom, a contrast-difference level of 1.0% (difference in the CT number = 10 HU), and objects 3.0-9.0 mm in diameter. RESULTS: On step-and-shoot scans the mean detection fraction with CNR-based AEC remained almost constant from 88 to 99 % regardless of the lesion size. We observed the same trend on helical scans, the mean detection fraction with CNR-based AEC exhibited a high score from 91 to 100%. Although CNR-based AEC maintains higher CNR for smaller size or lower contrast lesion, radiation dose on 3 mm lesion resulted in about 13 times larger than that of 9 mm lesion size. CTDI(vol) for the CNR-based AEC technique changed dramatically with the SD(Z) from 7.5 to 100.0 mGy for step-and-shoot scans and from 9.1 to 121.5 mGy for helical scans. CONCLUSIONS: From the viewpoint of ROC analysis-based CNR for lesion detection, CNR-based AEC potentially provide image quality advantages for clinical implementation.
PURPOSE: To develop a new automatic exposure control (AEC) technique based on the contrast-to-noise ratio (CNR) and provide constant lesion detectability. METHODS: Lesion detectability is affected by factors such as image noise, lesion contrast, and lesion size. We performed ROC analysis to assess the relationship between the optimum CNR and the lesion diameter at various levels of lesion contrast. We then developed a CNR-based AEC algorithm based on lesion detectability. Using CNR- based AEC algorithm, we performed visual evaluation of low-contrast detectability by 5 radiologists on a low-contrast module of the Catphan phantom, a contrast-difference level of 1.0% (difference in the CT number = 10 HU), and objects 3.0-9.0 mm in diameter. RESULTS: On step-and-shoot scans the mean detection fraction with CNR-based AEC remained almost constant from 88 to 99 % regardless of the lesion size. We observed the same trend on helical scans, the mean detection fraction with CNR-based AEC exhibited a high score from 91 to 100%. Although CNR-based AEC maintains higher CNR for smaller size or lower contrast lesion, radiation dose on 3 mm lesion resulted in about 13 times larger than that of 9 mm lesion size. CTDI(vol) for the CNR-based AEC technique changed dramatically with the SD(Z) from 7.5 to 100.0 mGy for step-and-shoot scans and from 9.1 to 121.5 mGy for helical scans. CONCLUSIONS: From the viewpoint of ROC analysis-based CNR for lesion detection, CNR-based AEC potentially provide image quality advantages for clinical implementation.
Authors: R Pauwels; L Seynaeve; J C G Henriques; C de Oliveira-Santos; P C Souza; F H Westphalen; I R F Rubira-Bullen; R F Ribeiro-Rotta; M I B Rockenbach; F Haiter-Neto; P Pittayapat; H Bosmans; R Bogaerts; R Jacobs Journal: Dentomaxillofac Radiol Date: 2015-06-19 Impact factor: 2.419