ABSTRACT
Reject analysis is as a quality indicator and critical tool for dose and image quality optimization in radiology departments. By reducing image rejection rate (RR), radiation dose to patients can be reduced effectively, yielding increased total cost-effectiveness. The aims of this study were to assess the rate of image rejection at 2 direct digital radiography (DR) departments to find the sources of rejection and to observe how radiology students and radiographers deal with image rejection. Two radiology departments were surveyed during a 3-month period for all imaging procedures. Type of examination, numbers, and reasons for digital image rejection were obtained by systems and questionnaire. A predefined questionnaire, including 13 causes for rejection, was filled by radiographers and students. Out of the 14 022 acquired images, 1116 were rejected, yielding an overall RR of 8%. Highest RRs were found for examination of cervical spine and lumbosacral. Positioning errors and improper patient preparation were the main reasons for digital image rejection. The image RR was small, but there is a need for optimizing radiographic practice, and enhancing radiographer's knowledge may enhance the performance.
ABSTRACT
The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22%, 34%, and 45% of features were most robust (COV ≤ 5%) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100%) and 76% of which showed large variations (COV > 20%) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis.
