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KMID : 1149520190030020035
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Cardiovasclar Imaging Asia
2019 Volume.3 No. 2 p.35 ~ p.46
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Ideal Bolus Geometry Predicted from In vitro Pulsatile Flow Phantom and Artificial Neural Networks for the Optimization of Image Acquisition Protocols for Aortic Contrast-Enhanced Computed Tomography Angiography
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Youn Sung-Won
Kwon Ju-Young
Kim Jung-Hoon
Park Ji-Eun
Ahn Do-Hyun
Lee Jong-Min
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Abstract
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Objective: This study sought to explore the novel use of artificial neural networks (ANNs) to develop a contrast-enhanced computed tomography (CT) angiographic (CECTA) protocol based on ideal bolus geometry.
Materials and Methods: An aortic phantom connected to a closed-circuit pulsatile flow system was developed to simulate the bolus geometry of the human abdominal aorta. A total of 135 CECTA datasets were obtained using a 16-row multidetector CT scanner, and timeenhancement curves (TECs) were generated using varying input conditions including heart rate (HR), iodine delivery rate (IDR) and concentration (IC), and tube potential (kVP). Time points and density values including peak enhancement (PE) and time-to-peak (TTP) were assessed as a function of injection and scan protocols. Statistical analysis was performed using correlation and linear regression analyses. By using data from phantom experiments, machine learning produced networks between four input (HR, IC, IDR, and kilovoltage) and five output [TTP-time-to-foot (TTF), PE, (PE)/(TTT-TTF), maximal-upslope-gradient (MUG), and peak-plateau-length (PPL)] conditions. The bolus geometry index was defined as (TTT-TTF)/Σ(PE, (PE)/(TTT-TTF), MUG, PPL). The lowest bolus geometry index value was considered ideal in ANN testing.
Results: The geometric changes on TECs were observed based on changes in HR, IDR, IC, and kilovoltage value. PE was closely related to IDR (B=17.471) and kVp (B=?0.208) (corrected R2=0.919; all p<0.001). TTF, TTP, and PPL were related to HR and IDR, respectively. HR and IDR remained contributing factors after multiple linear regression analysis (corrected R2=0.901, 0.815, and 0.363; all p<0.001). Among 39690 total datasets produced following ANN training, the combination of IC, HR, tube potential, and IDR in the 38010th dataset resulted in the lowest bolus geometry index. Tables of input variables are presented after modification to clinically acceptable ranges.
Conclusion: ANN of phantom experiments showed the potential to determine optimal CECTA parameters for ideal bolus geometry individualized for each subject.
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KEYWORD
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CT angiography, Neural network, CT protocol, Imaging phantom, Aorta
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