Abstract
Automatic exposure control based on tube current modulation (TCM) can effectively reduce dose while maintaining image quality. Conventional TCM uses total exposure from the tube and noise in the center of CT slices as surrogates of dose and image quality, respectively. In this abstract, we present an automated method to optimize TCM at the organ level, offering increased flexibility and aligning with the concept of organ-specific radiation risk assessment. We applied our method to a retrospective CT dataset and incorporated automatic organ segmentation, Monte Carlo simulation for dose calculation, and an empirical model for noise estimation. This method was fully automated and readily scalable to massive clinical data, allowing the generation of ground-truth data for any data-driven approach to prospective planning, including methods utilizing scout images.
Original language | English |
---|---|
Title of host publication | Medical Imaging 2024 |
Subtitle of host publication | Physics of Medical Imaging |
Editors | Rebecca Fahrig, John M. Sabol, Ke Li |
ISBN (Electronic) | 9781510671546 |
DOIs | |
State | Published - 2024 |
Event | Medical Imaging 2024: Physics of Medical Imaging - San Diego, United States Duration: Feb 19 2024 → Feb 22 2024 |
Publication series
Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
---|---|
Volume | 12925 |
ISSN (Print) | 1605-7422 |
Conference
Conference | Medical Imaging 2024: Physics of Medical Imaging |
---|---|
Country/Territory | United States |
City | San Diego |
Period | 2/19/24 → 2/22/24 |
Bibliographical note
Publisher Copyright:© 2024 SPIE.
Funding
This work was supported by GE HealthCare.
Funders | Funder number |
---|---|
GE Healthcare |
Keywords
- computed tomography
- image quality
- organ dose
- Tube current modulation
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Biomaterials
- Radiology Nuclear Medicine and imaging