Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study

Huiyuan Zhu; Zike Huang; Qunhui Chen; Weiling Ma; Jiahui Yu; Shiqing Wang; Guangyu Tao; Jun Xing; Haixin Jiang; Xiwen Sun; Jing Liu; Hong Yu; Lin Zhu

doi:10.1016/j.acra.2024.11.042

Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study

Acad Radiol. 2024 Dec 13:S1076-6332(24)00886-9. doi: 10.1016/j.acra.2024.11.042. Online ahead of print.

Authors

Huiyuan Zhu¹, Zike Huang², Qunhui Chen¹, Weiling Ma¹, Jiahui Yu¹, Shiqing Wang¹, Guangyu Tao¹, Jun Xing¹, Haixin Jiang¹, Xiwen Sun³, Jing Liu⁴, Hong Yu¹, Lin Zhu⁵

Affiliations

¹ Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (H.Z., Q.C., W.M., J.Y., S.W., G.T., J.X., H.J., H.Y., L.Z.).
² Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China (Z.H.); Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China (Z.H.).
³ Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200092, China (X.S.).
⁴ Department of Radiology, Zhabei Central Hospital, Shanghai 200070, China (J.L.).
⁵ Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (H.Z., Q.C., W.M., J.Y., S.W., G.T., J.X., H.J., H.Y., L.Z.). Electronic address: 728009507@shsmu.edu.cn.

PMID: 39674695
DOI: 10.1016/j.acra.2024.11.042

Abstract

Rationale and objectives: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.

Materials and methods: Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard. Objective assessment, including image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), and subjective assessment using five-point scales by five radiologists were performed. Detection and false-positive rate of subsolid nodules, and morphologic features of nodules were recorded.

Results: 102 patients (mean age, 57.0 ± 12.3 years) with 358 subsolid nodules in SDCT were enrolled. The mean effective dose of SDCT and LDCT were 5.37 ± 0.80mSv and 0.86 ± 0.14mSv, respectively (P < 0.001). DLIR-H showed the lowest noise, highest CNRs, SNRs, and subjective scores among LDCT groups (all P < 0.001), almost approaching comparability with SDCT. The detection rates for DLIR-H, DLIR-M, ASIR-V-50%, and FBP were 76.5%, 76.3%, 83.8%, and 72.1%, respectively (P < 0.001), with false-positive rate of 2.5%, 2.2%, 8.3%, and 1.1%, respectively (P < 0.001). DLIR-H showed the highest detection rates for morphologic features (79.4%-95.2%) compared to DLIR-M (74.6%-88.9%), ASIR-V-50% (72.0%-88.4%), and FBP (66.1%-84.1%) (all P ≤ 0.001).

Conclusion: Sub-milliSievert LDCT with DLIR-H offers substantial dose reduction without compromising image quality. It is promising for evaluating subsolid nodules with a high detection rate and better identification of morphologic features.

Keywords: Computed tomography; Deep learning; Radiation dosage; Reconstruction; Subsolid nodule.