Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study

Article type: Research Article

Authors: Li, Haoyan^{a; 1} | Li, Zhentao^{b; 1} | Gao, Shuaiyi^a | Hu, Jiaqi^a | Yang, Zhihao^a | Peng, Yun^{a; 2; *} | Sun, Jihang^{a; 2; *}

Affiliations: [a] Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China | [b] Department of Radiology, Peking University People’s Hospital, Beijing, China

Correspondence: [*] Corresponding authors: Jihang Sun, Yun Peng, Beijing Children’s Hospital, Capital Medical University, Department of radiology, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China. E-mails: jihangsuns@163.com, (J.S.) ppengyun@hotmail.com, (Y.P.).

Note: [1] Dr. Haoyan Li and Dr. Zhentao Li have made the equal contribution as the First author in this study.

Note: [2] Dr. Jihang Sun and Dr. Yun Peng have made the equal contribution as the corresponding author in this study.

Abstract: OBJECTIVES:To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS:An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were computed and compared among reconstructions. RESULTS:NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d’ than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d’ increased for acrylic insert, and d’ of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS:DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d’.

Keywords: Multidetector computed tomography, image enhancement, image reconstruction, deep learning

DOI: 10.3233/XST-230333

Journal: Journal of X-Ray Science and Technology, vol. 32, no. 3, pp. 513-528, 2024