Saudi Cultural Missions Theses & Dissertations
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Item Restricted The Influence of Categorization, Metrology, and Analysis Methods on the Quality and Variability Characterization of Clinical Imaging(Saudi Digital Library, 2026) Alsaihati, Njood; Samei, EhsanComputed tomography (CT) imaging is one of the most widely used diagnostic tools in modern medicine. Despite its clinical value, substantial variability exists in CT imaging practices across institutions, scanner models, protocols, and patient populations. This variability complicates efforts to evaluate imaging performance, ensure consistency, and deliver personalized imaging care. At the same time, the increasing availability of large clinical imaging datasets has created new opportunities for quality monitoring, research, and data-driven optimization. However, the ability to extract meaningful insights from these data remains limited by inconsistencies in how imaging studies are categorized, how key parameters are measured, and how imaging data are analyzed. These challenges hinder the ability to answer fundamental clinical questions, interpret variability in imaging practices, and support quality assurance and benchmarking initiatives. This dissertation investigates how categorization, metrology, and analysis methods influence the characterization and interpretation of quality and variability in clinical CT imaging data. The first component of this work focuses on improving the characterization and interpretation of CT imaging practices through enhanced data categorization, measurement, and analysis within radiation dose monitoring systems (RDMSs). RDMSs are widely used in radiology practices to collect and analyze radiation dose information from clinical imaging systems. However, most commercially available RDMS platforms monitor radiation exposure metrics without incorporating image quality information, limiting their ability to support comprehensive imaging optimization. In this work, RDMS functionality was extended to incorporate image quality metrics alongside radiation dose measurements through the design of role-specific visualization interfaces tailored to radiologists, technologists, and medical physicists. The proposed interface was evaluated using a Likert scale assessment framework to measure the effectiveness of fifteen charts designed to address key clinical questions related to imaging performance. The interface received an overall average usability score of 7.8 out of 10.0, with radiologists rating it highest at 8.4, technologists at 7.6, and medical physicists at 7.5. These results demonstrate how radiation dose assessment can be performed in conjunction with image quality evaluation through customizable visualization tools designed to meet the needs of different radiology professionals. The second component of this dissertation addresses metrology challenges associated with patient size characterization in CT imaging. Image quality in CT is strongly influenced by radiation output and patient attenuation; however, patient body habitus varies widely, and multiple metrics have been proposed to quantify patient size. Using a virtual imaging trial framework, six computational human models representing a range of adult body habitus were imaged using a validated CT simulator modeling a representative clinical CT system. Simulated scans were performed across multiple fixed exposure levels for several anatomical regions with and without noise. A range of patient size metrics were evaluated — including effective diameter and water-equivalent diameter computed at individual image slices, at the center slice, and averaged or summarized across slices — alongside patient weight and body mass index as size surrogates. Predictive models were developed to relate image noise, tube current, and patient size. Predictive performance varied substantially across anatomical regions. Slice-level water-equivalent diameter showed the lowest prediction errors and served as the reference metric for evaluating other size measures. In the head region, slice-level effective diameter showed the closest agreement to this reference. In the neck and head-neck-shoulder regions, most size metrics exhibited substantially higher errors except for slice-level effective diameter. In the chest region, weight and body mass index exhibited the highest percentage errors. These findings demonstrate that the choice of patient size metric significantly influences the accuracy of noise and tube current predictions and should be selected with consideration of anatomical context. The third component examines how measurement choices influence the estimation of radiation risk surrogates. Using the same virtual imaging framework, simulated CT scans were performed across a range of exposure levels with both average and strong tube current modulation settings. Organ dose estimates obtained from Monte Carlo simulations were used to calculate effective dose and risk index metrics. Multiple patient size metrics were evaluated for their ability to predict these radiation risk surrogates across head, neck, head-neck, and chest imaging regions. The predictive accuracy of size metrics varied substantially depending on anatomical region, risk metric, and exposure conditions. In the head, center-slice effective diameter yielded the lowest error for effective dose, while averaged and median effective and water-equivalent diameters best predicted the risk index. Body mass index showed the poorest performance for both. Performance patterns differed across the remaining anatomical regions, with no single metric consistently outperforming others. Stronger tube current modulation settings also increased variability in prediction accuracy. These findings demonstrate that the predictive value of patient size metrics for CT radiation risk depends on anatomical region, exposure level, and tube current modulation schema, emphasizing how size metric selection affects both the accuracy and uncertainty of radiation risk estimates. The final component of this dissertation addresses the structural representation of imaging data through the development of an ontology-based framework for CT data profiling and characterization. The proposed ontology, Operational Ontology for Radiology (OOR), models the radiology imaging workflow from study ordering through image interpretation using explicitly defined entities, relationships, and annotated data properties that capture imaging tasks, acquisition parameters, and workflow context. To illustrate the analytic implications of categorization, CT examinations mapped to a single registry category were analyzed. Although these examinations were treated as a single category within the registry, they originated from five institutional protocols with distinct clinical tasks and indications, contrast phase structures, and anatomic coverage. When all examinations were analyzed together, a single summary dose value could be interpreted as typical for this examination type. However, when the same examinations were regrouped using ontology-defined attributes such as imaging task, contrast phases, and scan coverage, summary dose values varied substantially across task-specific groups. These results demonstrate that analytic conclusions about imaging dose and variability depend strongly on how imaging examinations are categorized. The feasibility of implementing OOR in clinical environments was evaluated by assessing the availability of ontology-defined data properties within the clinical imaging environment at one comprehensive academic medical center. Among the evaluated properties representing key elements of the CT imaging workflow, roughly half were directly available in structured form, one third required additional standardization, a small portion were indirectly available but standardized, and the remainder were not readily available in current systems. These findings demonstrate that a substantial portion of the proposed ontology can be supported using existing clinical data infrastructures while also identifying areas where improved standardization is needed to enable consistent large-scale analysis of imaging practices. Collectively, this work demonstrates that the interpretation of variability in clinical imaging depends critically on how imaging data are categorized, how measurements are defined, and how analyses are performed. By integrating improved monitoring tools, evaluating measurement strategies for patient size characterization, and developing an ontology framework for imaging data representation, this dissertation provides methods to support more transparent and consistent characterization of CT imaging practices. These contributions enable more interpretable benchmarking of imaging performance, support data-driven quality improvement, and establish a framework for scalable analysis of clinical imaging data.14 0