Organ doses from CT localizer radiographs: Development, validation, and application of a Monte Carlo estimation technique






Published Version (Please cite this version)


Publication Info

Hoye, Jocelyn, Shobhit Sharma, Yakun Zhang, Wanyi Fu, Francesco Ria, Anuj Kapadia, W Paul Segars, Joshua Wilson, et al. (2019). Organ doses from CT localizer radiographs: Development, validation, and application of a Monte Carlo estimation technique. MEDICAL PHYSICS, 46(11). pp. 5262–5272. [10.1002/mp.13781]](]) Retrieved from

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.



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.


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.

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.