Computed tomography contrast enhancement pattern of the uterus in premenopausal women in relation to menstrual cycle and hormonal contraception.

BACKGROUND: There are different types of computed tomography (CT) contrast enhancement patterns of the uterus. It is not known whether these are hormonally dependent. PURPOSE: To assess the relationship between these patterns and the menstrual cycle in non-users of hormonal contraception, and the possible impact of hormonal contraception. MATERIAL AND METHODS: Prospective observational study of abdominal CT scans of 53 premenopausal women of whom 28 were non-users and 25 users of hormonal contraception. The non-users were divided according to menstrual cycle phase: follicular (n = 12); ovulatory (n = 1); and luteal (n = 12). The pattern and intensity of contrast enhancement of the uterine myometrium were assessed. RESULTS: The dominant pattern of contrast enhancement of the myometrium was the diffuse homogeneous type in both non-users and users. The intensity of the enhancement measured in Hounsfield units (HU) was higher in the follicular phase (median 102, range 73-130) compared to the luteal phase in non-users (median 92, range 57-130); however this was not statistically significant (P = 0.2). The HU values observed in users (median 95, range 45-160) were at the same levels compared to those of the luteal phase in non-users. CONCLUSION: The dominant pattern of contrast enhancement in the portal venous phase of the myometrium in fertile ages is the diffuse homogeneous type and is independent of menstrual cycle phase or the use of hormonal contraception. However, these factors seem to play a role in the intensity of contrast enhancement, with a tendency of higher HU values in the follicular phase of non-users.

NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations.

Image noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, "NoiseNet", was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study. The model is a 3D U-Net that automatically segments the aortic root and measures attenuation (Hounsfield Units, HU), noise (standard deviation of HU, HUsd) and signal-to-noise ratio (SNR, HU/HUsd) in the aortic lumen, close to the left coronary ostium. NoiseNet was then applied to 529 CCTA studies previously categorized into three subgroups: fully diagnostic, diagnostic with excluded parts and non-diagnostic. There was excellent correlation between NoiseNet and manual measurements of noise (r = 0.948; p < 0.001) and SNR (r = 0.948; <0.001). There was a significant difference in noise levels between the image quality subgroups: fully diagnostic 33.1 (29.8-37.9); diagnostic with excluded parts 36.1 (31.5-40.3) and non-diagnostic 42.1 (35.2-47.7; p < 0.001). Corresponding values for SNR were 16.1 (14.0-18.0); 14.0 (12.4-16.2) and 11.1 (9.6-14.0; p < 0.001). ROC analysis for prediction of a non-diagnostic study showed an AUC for noise of 0.73 (CI 0.64-0.83) and for SNR of 0.80 (CI 0.71-0.89). In conclusion, NoiseNet can perform noise and SNR measurements with high accuracy. Noise and SNR impact image quality and automatic measurements may be used to identify CCTA studies with low image quality.