Effect of miscentering and low-dose protocols on contrast resolution in computed tomography head examination.

BACKGROUND: Unoptimized protocols, including a miscentered position, might affect the outcome of diagnostic in CT examinations. In this study, we investigate the effects of miscentering position during CT head examination on the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). METHOD: We simulate the CT head examination using a water phantom with a standard protocol (120 kVp/180 mAs) and a low dose protocol (100 kVp/142 mAs). The table height was adjusted to simulate miscentering by 5 cm from the isocenter, where the height was miscentered superiorly (MCS) at 109, 114, 119, and 124 cm, and miscentered inferiorly (MCI) at 99, 94, 89, and 84 cm. Seven circular regions of interest were used, with one drawn at the center, four at the peripheral area of the phantom, and two at the background area of the image. RESULTS: For the standard protocol, the mean CNR decreased uniformly as table height increased and significantly differed (p < 0.05) at +20 cm for MCS (435.70 ± 9.39) and -20 cm for MCI (438.91 ± 10.94) from the isocenter. Similarly, significant reductions (p < 0.05) were also noted for SNR for MCS (at +20 cm) and MCI (at -20 cm). For the low dose protocol, both CNR and SNR were significantly reduced (p < 0.05) at table heights of +20 and -20 cm from the isocenter. CONCLUSION: Miscentering is proven to significantly affect the image quality in both low and standard dose protocols for head CT procedure. This study implies that accurate patient centering is one of the approaches that can improve CT optimization practice.

A comparison study of radiation effective dose in ECG-Gated Coronary CT Angiography and calcium scoring examinations performed with a dual-source CT scanner.

In this report we have evaluated radiation effective dose received by patients during ECG-gated CCTA examinations based on gender, heart rate, tube voltage protocol and body mass index (BMI). A total of 1,824 patients were retrospectively recruited (1,139 men and 685 women) and they were divided into Group 1 (CCTA with calcium scoring), Group 2 (CCTA without calcium scoring) and Group 3 (only calcium scoring), where the association between gender, heart rate, tube voltage protocol and body mass index (BMI) were analysed. Examinations were performed using a retrospective ECG-gated CCTA protocol and the effective doses were calculated from the dose length product with a conversion coefficient of 0.026 mSv.mGy-1cm-1. No significant differences were observed in the mean effective dose between gender in all groups. The mean estimated dose was significantly higher when the heart rate was lower in Group 1 (p < 0.001) and Group 2 (p = 0.002). There were also significant differences between the mean effective dose in tube voltage protocol and BMI among the three groups. The mean effective dose was positively correlated with BMI (p < 0.001), but inversely related to the heart rate. This study supported the theory that a high heart rate, low tube voltage and low BMI could significantly reduce radiation dose exposure.

Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions.

Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database.