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Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.
Zheng, Zhijuan; Liang, Yuying; Wu, Zhehao; Han, Qijia; Ai, Zhu; Ma, Kun; Xiang, Zhiming.
Afiliación
  • Liang Y; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
  • Han Q; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
  • Ai Z; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
  • Ma K; CT Imaging Research Center, GE HealthCare China, Guangzhou, China.
  • Xiang Z; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
J Comput Assist Tomogr ; 2024 Aug 02.
Article en En | MEDLINE | ID: mdl-39095065
ABSTRACT

OBJECTIVE:

The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).

METHODS:

One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature.

RESULTS:

Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose.

CONCLUSIONS:

DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Comput Assist Tomogr Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Comput Assist Tomogr Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos