A mathematical framework to quantitatively balance clinical and radiation risk in Computed Tomography


Purpose: Risk in medical imaging is a combination of radiation risk and clinical risk, which is largely driven by the effective diagnosis. While radiation risk has traditionally been the main focus of Computed Tomography (CT) optimization, such a goal cannot be achieved without considering clinical risk. The purpose of this study was to develop a comprehensive mathematical framework that considers both radiation and clinical risks based on the specific task, the investigated disease, and the interpretive performance (i.e., false positive and false negative rates), tested across a representative clinical CT population. Methods and Materials: The proposed mathematical framework defined the radiation risk to be a linear function of the radiation dose, the population prevalence of the disease, and the false positive rate. The clinical risk was defined to be a function of the population prevalence, the expected life-expectancy loss for an incorrect diagnosis, and the interpretative performance in terms of the AUC as a function of radiation dose. A Total Risk (TR) was defined as the sum of the radiation risk and the clinical risk. With IRB approval, the mathematical function was applied to a dataset of 80 adult CT studies investigating localized stage liver cancer (LLC) for a specific false positive rate of 5% reconstructed with both Filtered Back Projection (FBP) and Iterative Reconstruction (IR) algorithm. Linear mixed effects models were evaluated to determine the relationship between radiation dose and radiation risk and interpretative performance, respectively. Lastly, the analytical minimum of the TR curve was determined and reported. Results: TR is largely affected by clinical risk for low radiation dose whereas radiation risk is dominant at high radiation dose. Concerning the application to the LLC population, the median minimum risk in terms of mortality per 100 patients was 0.04 in FBP and 0.03 in IR images; the corresponding CTDIvol values were 38.5 mGy and 25.7 mGy, respectively. Conclusions: The proposed mathematical framework offers a complete quantitative description of risk in CT enabling a comprehensive risk-to-benefit assessment essential in the effective justification of radiological procedures and in the design of optimal clinical protocols. Clinical Relevance/Application: The quantification of both radiation and clinical risk using comparable units allows the calculation of the overall risk paving the road towards a comprehensive risk-to-benefit assessment in CT.








Francesco Ria

Assistant Professor of Radiology

Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.

Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.


Alaattin Erkanli

Associate Professor of Biostatistics & Bioinformatics

Areas of research interests include Bayesian hierarchical models for longitudinal data, Bayesian optimal designs, finite mixtures and Mixtures of Dirichlet Processes, Markov transition models, nonparametrics smoothing and density estimation, survival analysis for recurrent-event data, biomarker selection and detecting early ovarian cancer.


Ehsan Samei

Reed and Martha Rice Distinguished Professor of Radiology

Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American medical physicist. He is the Reed and Martha Rice Distinguished Professor of Radiology, and Professor of Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University. He serves as the Chief Imaging Physicist for Duke University Health System, the Director of the Carl E Ravin Advanced Imaging Laboratories and the Center for Virtual Imaging Trials (CVIT), and co-PI of one the five Centers of Excellence in Regulatory Science and Innovation (CERSI), Triangle CERSI. He is certified by the American Board of Radiology, recognized as a Distinguished Investigator by the Academy of Radiology Research, and awarded Fellow by five professional organization. He founded/co-founded the Duke Medical Physics Program, the Duke Imaging Physics Residency Program, the Duke Clinical Imaging Physics Group, the Center for Virtual Imaging Trials, and the Society of Directors of Academic Medical Physics Programs (SDAMPP). He has held senior leadership positions in the AAPM, SPIE, SDAMPP, and RSNA, including election to the presidency of the SEAAPM (2010-2011), SDAMPP (2011), and AAPM (2023).

Dr. Samei’s scientific expertise include x-ray imaging, theoretical imaging models, simulation methods, and experimental techniques in medical image formation, quantification, and perception.  His research aims to bridge the gap between scientific scholarship and clinical practice, in the meaningful realization of translational research, and in clinical processes that are informed by scientific evidence. He has advanced image quality and safety metrics and radiometrics that are clinically relevant and that can be used to design, optimize, and monitor interpretive and quantitative performance of imaging techniques. These have been implemented in advanced imaging performance characterization, procedural optimization, and clinical dose and quality analytics. His most recent research interests have been virtual clinical trial across a broad spectrum of oncologic, pulmonary, cardiac, and vascular diseases, and developing methodological advances that provide smart fusions of principle-informed and AI-based, data-informed approaches to scientific inquiry.

Dr. Samei has mentored over 140 trainees (graduate and postgraduate). He has >1400 scientific publications including >360 referred journal articles, ~600 conference presentations, and 4 books. Citations to his work is reflected in an h-index of 74 and a Weighted Relative Citation Ratio of 613. His laboratory of over 20 researchers has been supported continuously over two decades by 44 extramural grants, culminating in a NIH Program Project grant in 2021 to establish the national Center for Virtual Imaging Trials (CVIT), joining a small number of prominent Biomedical Technology Research Centers across the nation. In 2023, he, along with 3 other PIs, was awarded to lead one of five national Centers of Excellence in Regulatory Science and Innovation (Triangle CERSI) by the FDA.

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