Radiation Dose Reduction in CT and Its Effects on Machine Learning
Dr. Michael McNitt-Gray, a professor in the Department of Radiological Sciences at the University of California, Los Angeles (UCLA), will speak at the February NCI Imaging and Informatics Community Webinar (IICW). He will discuss radiation dose reduction in Computed Tomography (CT) and its effects on quantitative imaging and artificial intelligence (AI)/machine learning (ML) algorithms.
Join this event if you’re interested in:
- examples of the effects of radiation reduction technologies on the use of AI/ML.
- how to mitigate the effects of radiation reduction technologies.
- how mitigation could enable more generalizable use of quantitative imaging methods.
Reducing radiation dose from CT is an important goal. There have been several developments helping to reduce exposure and dose. However, we continue to work to better understand the effects of these technologies. How do these reduction methods impact our ability to use AI and ML on CT images?
Both the Center for Biomedical Informatics and Information Technology and the Cancer Imaging Program organize the monthly NCI IICW. During the first Monday of every month, this event features scientific presentations and project updates. To receive updates on future topics and to access previous presentations and recordings, visit the IICW webpage.
This event is free and open to the public.
Dr. McNitt-Gray is a professor in the Department of Radiological Sciences at the David Geffen School of Medicine at UCLA. His research interests include CT imaging and the use of AI/ML algorithms in the detection, diagnosis, and evaluation of disease. He is a fellow of the American Association of Physics in Medicine and the American College of Radiology (ACR). He was an investigator on the Lung Image Database Consortium project, a member of the Physics Subcommittee of the National Lung Screening Trial, and an investigator in the Quantitative Imaging Network. He also served on the Physics Subcommittee of the ACR’s CT Accreditation Program.
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