Clinical Decision Making in CT: Risk Assessment Comparison Across 12 Risk Metrics in Patient Populations

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Purpose The Medical Physics 3.0 initiative aims to enhance direct physicist involvement in clinical decision making to improve patient care. In this involvement, it is crucial to achieve effective and patient-specific radiation risk assessment. CT risk characterization presents a variety of metrics, many of which used as radiation risk surrogates; some are related to the device output (CTDI), whereas others include patient organ risk-, age-, and gender-factors (Effective Dose, Risk Index). It is unclear how different metrics can accurately reflect the radiological risk. This study compared how twelve metrics characterize risk across CT patient populations to inform effective clinical decision making in radiology. Methods This IRB-approved study included 1394 adult CT examinations (abdominopelvic and chest). Organ doses were calculated using Monte Carlo methods. The following risk surrogate metrics were calculated: CTDIvol, DLP, SSDE, DLP-based Effective Dose (EDk), organ-dose-based ED (EDOD), dose to defining organ (stomach- and lungs-ODD), organ-dose-based Risk Index (RI), and 20 y.o. patient Risk Index (RIr). Furthermore, ODD,0, ED0, and RI0 were calculated for a reference patient (ICRP 110). Lastly, an adjusted ED (ED') was computed as the product of RI/RIr and EDOD. A linear regression was applied to assess each metric’s dependency to RI, assumed to be the closest patient risk surrogate. The normalized-slope (nS) and a Minimum Risk Detectability Index (MRDI=RMSE/slope) were calculated for each fit. Results The analysis reported significant differences between the metrics. ED’ showed the best concordance with RI in terms of nS and MRDI. Across all metrics and protocols, nS ranged between 0.37(SSDE) to 1.29(RI0); MRDI ranged between 39.11(EDk) to 1.10(ED’) cancers per 105 patients per 0.1Gy. Conclusion Radiation risk characterization in CT populations is strongly affected by the index used to describe it. When involved in clinical decisions, medical physicists should exercise care in ascribing an implicit risk to factors that do not closely reflect risk.






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William Paul Segars

Professor in Radiology

Our current research involves the use of computer-generated phantoms and simulation techniques to investigate and optimize medical imaging systems and methods. Medical imaging simulation involves virtual experiments carried out entirely on the computer using computational models for the patients as well as the imaging devices. Simulation is a powerful tool for characterizing, evaluating, and optimizing medical imaging systems. A vital aspect of simulation is to have realistic models of the subject's anatomy as well as accurate models for the physics of the imaging process. Without this, the results of the simulation may not be indicative of what would occur in actual clinical studies and would, therefore, have limited practical value. We are leading the development of realistic simulation tools for use toward human and small animal imaging research.

These tools have a wide variety of applications in many different imaging modalities to investigate the effects of anatomical, physiological, physical, and instrumentational factors on medical imaging and to research new image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. We are currently applying them to the field of x-ray CT. The motivation for this work is the lack of sufficiently rigorous methods for optimizing the image quality and radiation dose in x-ray CT to the clinical needs of a given procedure. The danger of unnecessary radiation exposure from CT applications, especially for pediatrics, is just now being addressed. Optimization is essential in order for new and emerging CT applications to be truly useful and not represent a danger to the patient. Given the relatively high radiation doses required of current CT systems, thorough optimization is unlikely to ever be done in live patients. It would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to the optimization problem is through the use of realistic computer simulation tools developed in our work.


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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