PinS Diagnostic - The Balancing Act: Optimizing CT Image Quality and Dose
C Burton1*, G Grousset2*, J Vaishnav3*, I Bechwati4*, F Rupcich5*, (1) Boston Children's Hospital, Memphis, TN, (2) Siemens Healthineers, (3) Canon Medical Systems, USA, Tustin, CA, (4) NeuroLogica Corporation, Danvers, MA, (5) GE Healthcare, Waukesha, WI
Presentations
TU-A-Vendor Track-0 (Tuesday, 4/20/2021) 10:00 AM - 12:00 PM [Eastern Time (GMT-4)]
According to NCRP Report 160, the effective dose to the population coming from medical has increased 6 fold since the 1980's.[1] The dose coming from medical comprise of 15% with background at 83% in the 1980's, and in 2006 medical increased to nearly 50%. Half of that effective dose is coming from Computed Tomography (CT) due to the relative increase in the number of exams and the relatively high dose compared to radiography. Dose from CT has always been a concern to the medical imaging community. Medical physicists have always needed to figure out a way to keep within the ALARA principles while maintaining adequate image quality. There are two main components that have gone into lowering dose to the patient, the use of size-specific dose estimates (SSDE) from patient size surrogates and artificial intelligence (AI)-based algorithms that can reconstruct images to appear high quality. We will give an introduction to both SSDE and AI-based algorithms to demonstrate how they have been effective at lowering dose.
Learning objectives:
1. Understand the basics of SSDE and patient size surrogates (how to extract them from CT axial and CT localizer)
2. Understand the basics of AI-based algorithms and how they use the patient size surrogates for treatment planning
Reference(s)
1. Wall, Barry F. "Ionising radiation exposure of the population of the United States: NCRP report no. 160." (2009): 136-138.
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