Personalized, Scout-Based Dose Estimation for Prospective Optimization of CT Tube Current Modulation

被引:0
|
作者
Medrano, Maria [1 ]
Wang, Sen [1 ]
Imran, Abdullah-Al-Zubaer [1 ]
Stevens, Grant [2 ]
Tse, Justin Ruey [1 ]
Wang, Adam [1 ,3 ]
机构
[1] Stanford Univ, Dept Radiol, Stanford, CA 94305 USA
[2] GE HealthCare, Waukesha, WI 53188 USA
[3] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
关键词
X-ray CT; image quality; dose reduction; deep learning; tube current modulation; optimization;
D O I
10.1117/12.3006268
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
CT diagnostic imaging is a major contributor to ionizing radiation exposure in the United States. Unfortunately, a reduction in radiation dose often results in degraded image quality. Automatic Exposure Control (AEC) is the most commonly used method to balance image quality and dose in x-ray CT, generally by modifying the scan's tube current modulation (TCM) parameters. To allow current AEC techniques to be better personalized to the patient size, organ dose, and clinical task, our team previously proposed Scout-Dose and Scout-IQA to prospectively estimate dose and noise from frontal and lateral scouts, scan range, and TCM map. In this study, we evaluate for the first time the performance of our scout-based organ dose and noise predictions in an optimization framework to prospectively determine real-time, personalized TCM maps from a patient's acquired scouts and scan ranges.
引用
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页数:5
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