Image Quality Of Chest Radiographs Research Articles

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

A Dose Simulation X-Ray Software: An Innovating Tool to Reduce Chest Radiograph Exposure in Children.

Chest radiography is one of the most frequent x-ray examinations performed on children. Reducing the delivered dose is always a major task. The objective of our study was to determine the minimum dose to be delivered while maintaining the image quality of chest radiographs, using dose reduction simulation software. We included 60 children who had had a chest radiography in 5 groups established according to the diagnostic reference levels equitably represented by weight ranges. The software simulated for each radiograph 6 additional simulated photonic noise images corresponding to 100%, 80%, 64%, 50%, 40%, and 32% of the initial dose. The 360 radiographs were blindly scored by 2 radiologists, according to the 7 European quality criteria and a subjective criterion of interpretability, using a semiquantitative visual Lickert scale. There was no significant difference in scoring between the reference radiograph (100%) and simulated radiographs at 80% of the dose in children between 5 and 20 kg, 50% of the dose in children between 20 and 30 kg, and between simulated radiographs at 40% of the dose in children over 30 kg. Interobserver reproducibility was moderate to excellent. Chest radiography dose might be reduced by 20% in children between 5 and 20 kg, 50% in children between 20 and 30 kg, and 60% in children over 30 kg, without any difference in the image quality appreciation. Software that produced simulated x-ray with decreasing delivered dose is an innovating tool for an optimization process.

A total variation-undecimated wavelet approach to chest radiograph image enhancement

Most often medical images such as X-Rays have a low dynamic range and many of their targeted features are difficult to identify. Intensity transformations that improve image quality usually rely onwavelet denoising and enhancement typically use the technique of thresholding to obtain better quality medical images. A disadvantage of wavelet thresholding is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities. We utilize a total variation method and an undecimated wavelet image enhancing algorithm for improving the image quality of chest radiographs. Our approach achieves a high level chest radiograph image deniosing in lung nodules detection while preserving the important features. Moreover, our method results in a high image sensitivity that reduces the average number of false positives on a test set of medical data.

Relationship Between Tube Voltage and Physical Image Quality of Pulmonary Nodules on Chest Radiographs Obtained Using the Bone-Suppression Technique

Image quality of chest radiographs is affected by tube voltage. This study aimed to clarify the relationship between tube voltage and physical image quality of pulmonary nodules on bone-suppressed chest radiographs. An anthropomorphic chest phantom and a spherical simulated nodule, with a 12-mm diameter and approximately +100 Hounsfield units were used. The lung field of the phantom was divided into three areas, based on the overlap with the ribs in the chest radiograph. Ten positions of the simulated nodule were defined in each area. One hundred and twenty chest radiographs were acquired using four tube voltages (70 kVp, 90 kVp, 110 kVp, and 130 kVp) for a total of 30 nodule positions and were processed to create bone-suppressed images. Differences in contrast and contrast-to-noise ratio (CNR) were analyzed for all pairs of the four tube voltages using a two-sided Wilcoxon signed-rank test. In the area not overlapping with ribs, a statistically significant difference was observed only in contrast between tube voltage of 70 kVp and 90 kVp (p = 0.01). In the area overlapping with one or two ribs, the contrast and CNR tended to be higher at a lower tube voltage. In particular, the p values between the contrast at 70 kVp and that at the other three tube voltage settings were less than 0.01. For a relatively dense nodule, the contrast and CNR in the bone-suppressed chest radiograph were significantly improved with lower tube voltage in the lung field overlapping with the ribs.