Comparative Analysis of Digital versus Analog Radiography Image Quality in Chest Imaging

Thesis Abstract

The increasing adoption of digital radiography (DR) in clinical practice necessitates an rigorous evaluation of its image quality relative to traditional analog (film-based) radiography, particularly in the context of chest imaging where diagnostic accuracy is critically dependent on image clarity and detail. This study addresses the comparative effectiveness of digital versus analog radiography in capturing high-quality chest images, with the aim to determine whether digital systems provide statistically significant improvements in image quality parameters that influence diagnostic outcomes. The specific objectives include assessing image contrast, spatial resolution, noise levels, and diagnostic confidence associated with each modality, as well as exploring the influence of technical factors such as exposure parameters and post-processing techniques on image quality. The study employs a cross-sectional, comparative research design conducted over a six-month period within a tertiary hospital’s radiology department. The target population comprises all chest radiographs performed during the study period, totaling approximately 1,200 images, with a stratified random sampling technique selecting 600 images equally divided between digital and analog modalities. Data collection involves the use of standardized image quality assessment protocols, combining objective measurements—such as detective quantum efficiency (DQE), modulation transfer function (MTF), and signal-to-noise ratio (SNR)—with subjective evaluations by a panel of five experienced radiologists utilizing a validated scoring scale for diagnostic confidence and perceived image quality. Data analysis incorporates descriptive statistics to characterize the dataset, followed by inferential statistical techniques including paired t-tests to compare objective image quality metrics, and ANOVA to evaluate differences across various technical settings. Multiple regression analysis further investigates the relationship between technical factors and image quality outcomes, while inter-rater reliability is assessed through intraclass correlation coefficients (ICCs). The theoretical underpinning draws upon the Signal Detection Theory to interpret radiologist diagnostic confidence and the Technology Acceptance Model to explore factors influencing the choice of imaging modality. The study anticipates that digital radiography will demonstrate superior contrast resolution, higher SNR, and enhanced diagnostic confidence, although potential limitations related to noise artifacts and post-processing effects may diminish some advantages. Expected findings will elucidate significant differences in image quality metrics favoring digital radiography, with improved diagnostic confidence corroborated by subjective assessments. Such results are anticipated to validate the clinical efficacy of digital systems in chest imaging and provide quantifiable benchmarks against traditional film-based methods. The study’s contribution to knowledge lies in offering a comprehensive, evidence-based comparison grounded in both objective and subjective measures, bridging a gap in the literature particularly within the local clinical setting where digital adoption is relatively recent. The main conclusion will emphasize the potential of digital radiography to augment diagnostic accuracy in chest imaging, potentially informing policy decisions and equipment procurement protocols in healthcare facilities. The study recommends the continued integration of digital systems, coupled with targeted training to optimize imaging protocols and minimize artifacts. Additionally, the research suggests avenues for further investigation into cost-effectiveness analyses and the long-term clinical outcomes associated with each modality. Overall, this study aims to enhance radiological practice by providing a rigorous, empirically validated assessment of image quality in digital versus analog chest radiography, thereby contributing to improvements in patient care and diagnostic precision.