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  4. Deep Learning-Based Dual-Energy CT Imaging Using Only a Single-Energy CT Data

Deep Learning-Based Dual-Energy CT Imaging Using Only a Single-Energy CT Data

W Zhao1*, T Lv2 , L Shen3 , X Dai4 , K Cheng5 , M jia6 , y chen7 , L Xing8 , (1) Stanford University, Palo Alto, CA, (2) Southeast University,Nanjing, China,(3) ,Palo Alto, CA, (4) Stanford University, Mountain View, CA, (5) Stanford University, Stanford, CA, (6) ,Palo Alto, CA, (7) Southeast University,Nanjing, China,(8) Stanford Univ School of Medicine, Stanford, CA

Presentations

(Monday, 7/15/2019) 4:30 PM - 5:30 PM

Room: Exhibit Hall | Forum 2

Purpose: In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as single-energy CT (SECT) scanners. Here we purpose a deep learning approach to perform DECT imaging by using standard SECT data.

Methods: We developed a deep learning model to map low-energy image to high-energy image and trained the model using thousands of paired clinical DECT images. The performance of the deep learning-based DECT approach was studied using images from 22 patients who received contrast-enhanced abdomen DECT scan with a popular DE application: virtual non-contrast (VNC) imaging and contrast quantification. Quantitative comparisons between the predicted and the original CT images were assessed using HU accuracy in 60 region-of-interests (ROIs) on different types of tissues. VNC images and iodine maps quantification obtained from the original DECT images and the deep learning-based DECT images were also compared and quantitatively evaluated.

Results: The HU difference between the predicted and original high-energy CT images are 3.47, 2.95, 2.38 and 2.40 HU for ROIs on spine, aorta, liver and stomach, respectively. The HU differences between VNC images obtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and 2.92 HU for ROIs on spine, aorta, liver and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9%, suggesting high consistency between the predicted and the original high-energy CT images.

Conclusion: This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.

Keywords

Dual-energy Imaging

Taxonomy

IM- CT: Dual Energy and Spectral

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