Image noise and dose performance across a clinical population: patient size adaptation as a metric of CT performance.
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2017-02-24
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PURPOSE: Modern CT systems adjust x-ray flux accommodating for patient size to achieve certain image noise values. The effectiveness of this adaptation is an important aspect of CT performance and should ideally be characterized in the context of real patient cases. The objective of this study was to characterize CT performance with a new metric that includes image noise and radiation dose across a clinical patient population. MATERIALS AND METHODS: The study included 1526 examinations performed by three CT scanners (one GE Healthcare Discovery CT750HD, one GE Healthcare Lightspeed VCT, and one Siemens SOMATOM definition Flash) used for two routine clinical protocols (abdominopelvic with contrast and chest without contrast). An institutional monitoring system recorded all the data involved in the study. The dose-patient size and noise-patient size dependencies were linearized by considering a first order approximation of analytical models that describe the relationship between ionization dose and patient size, as well as image noise and patient size. A 3D-fit was performed for each protocol and each scanner with a planar function, and the Root Mean Square Error (RMSE) values were estimated as a metric of CT adaptability across the patient population. RESULTS: The data show different scanner dependencies in terms of adaptability: the RMSE values for the three scanners are between 0.0385 HU(1/2) and 0.0215 HU(1/2) . CONCLUSIONS: A theoretical relationship between image noise, CTDIvol and patient size was determined based on real patient data. This relationship may be interpreted as a new metric related to the scanners' adaptability concerning image quality and radiation dose across a patient population. This method could be implemented to investigate the adaptability related to other image quality indexes and radiation dose in a clinical population. This article is protected by copyright. All rights reserved.
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Ria, Francesco, Joshua Mark Wilson, Yakun Zhang and Ehsan Samei (2017). Image noise and dose performance across a clinical population: patient size adaptation as a metric of CT performance. Med Phys. 10.1002/mp.12172 Retrieved from https://hdl.handle.net/10161/13805.
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Francesco Ria
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 (AAPM) task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the AAPM task group 430 (Comprehensive quantification and dissemination of patient-model-based organ and effective dose estimations and their associated uncertainties for CT examinations), of the AAPM Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.
Joshua Wilson
Ehsan Samei
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-Director of the Triangle Centers of Excellence in Regulatory Science and Innovation (Triangle CERSI). Certified by the American Board of Radiology, he is recognized as a Distinguished Investigator by the Academy of Radiology Research, awarded Fellow by five professional organizations, and has been the recipient of the Jimmy O. Fenn Lifetime Achievement Award of SEAAPM and the Marie Sklodowska-Curie Award by IOMP. 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). He is ranked 11th among over 56,000 medical physicists worldwide for his lifetime contribution to medical physics.
Dr. Samei’s scientific expertise includes 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, facilitating the meaningful realization of translational research and informing clinical processes with 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 trials 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 150 trainees (graduate and postgraduate). He has about 1400 scientific publications, including over 400 refereed journal articles, over 600 conference presentations, and 4 books. Citations to his work is reflected in an h-index of 79 and a Weighted Relative Citation Ratio of 628. His laboratory has been supported continuously for over two decades by 47 extramural grants totaling over $49 million. Those include a Program Project grant from the NIH in 2021 to establish the National Center for Virtual Imaging Trials (CVIT), and a multi-institutional grant in 2023 from the FDA to establish the Triangle Center of Excellence in Regulatory Science and Innovation (Triangle CERSI), both joining a highly selective biomedical research and regulatory science centers nationwide.
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