The lack of a coherent, evidence-based approach for determining the "right" CT imaging protocol explains to a large degree the considerable variation in doses and techniques observed in clinical CT scanning and the recent injuries to patients undergoing CT brain imaging. A comprehensive, time-efficient, and translatable approach is needed to drive CT doses to the lowest levels that maintain acceptable diagnostic accuracy and to do so while keeping up with ever-changing technology. Our long term objective is to develop and validate quantitative methodsthat can quantitatively determine, for a specific diagnostic task, CT protocols that deliver the needed diagnostic accuracy at the lowest patient dose. The obiective of this project is to determine the tradeoffs between CT image quality and patient radiation dose in a way that moves beyond current simple models (e.g. standard deviation and CTDIvol) by investigating the relationships between task-based metrics of image quality, observer performance, patient size, and patient dose. This project was previously the 4th project in a POl proposal entitled Quantitative systematic development of dose-optimized CT imaging protocols {^ POl EB014221-01).

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB017095-03
Application #
8724217
Study Section
Special Emphasis Panel (ZEB1-OSR-D (M1))
Program Officer
Pai, Vinay Manjunath
Project Start
2012-09-20
Project End
2017-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
3
Fiscal Year
2014
Total Cost
$596,391
Indirect Cost
$194,663
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Chen, Baiyu; Yu, Lifeng; Leng, Shuai et al. (2016) Predicting detection performance with model observers: Fourier domain or spatial domain? Proc SPIE Int Soc Opt Eng 9783:
Chen, Baiyu; Leng, Shuai; Yu, Lifeng et al. (2016) An Open Library of CT Patient Projection Data. Proc SPIE Int Soc Opt Eng 9783:
Leng, Shuai; Chen, Baiyu; Vrieze, Thomas et al. (2016) Construction of realistic phantoms from patient images and a commercial three-dimensional printer. J Med Imaging (Bellingham) 3:033501
Ma, Chi; Yu, Lifeng; Chen, Baiyu et al. (2016) Impact of number of repeated scans on model observer performance for a low-contrast detection task in computed tomography. J Med Imaging (Bellingham) 3:023504
Chen, Baiyu; Ma, Chi; Leng, Shuai et al. (2016) Validation of a Projection-domain Insertion of Liver Lesions into CT Images. Acad Radiol 23:1221-9
Leng, Shuai; Shiung, Maria; Duan, Xinhui et al. (2015) Size-specific Dose Estimates for Chest, Abdominal, and Pelvic CT: Effect of Intrapatient Variability in Water-equivalent Diameter. Radiology 276:184-90
Favazza, Christopher P; Yu, Lifeng; Leng, Shuai et al. (2015) Automatic exposure control systems designed to maintain constant image noise: effects on computed tomography dose and noise relative to clinically accepted technique charts. J Comput Assist Tomogr 39:437-42
Chen, Baiyu; Yu, Zhicong; Leng, Shuai et al. (2015) Lesion Insertion in Projection Domain for Computed Tomography Image Quality Assessment. Proc SPIE Int Soc Opt Eng 9412:
Leng, Shuai; Yu, Lifeng; Vrieze, Thomas et al. (2015) Construction of Realistic Liver Phantoms from Patient Images using 3D Printer and Its Application in CT Image Quality Assessment. Proc SPIE Int Soc Opt Eng 2015:
Chen, Baiyu; Leng, Shuai; Yu, Lifeng et al. (2015) Lesion insertion in the projection domain: Methods and initial results. Med Phys 42:7034-42

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