Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction

• Published in: Journal of the Belgian Society of Radiology 2022-04, Vol.106 (1), p.15-15
• Main Authors: Yoo, Yeo Jin, Choi, In Young, Yeom, Suk Keu, Cha, Sang Hoon, Jung, Yunsub, Han, Hyun Jong, Shim, Euddeum
• Format: Article
• Language: English
• Subjects: computed tomography; deep learning-based image reconstruction; image quality; Original
• ISSN: 2514-8281; 2514-8281
• DOI: 10.5334/jbsr.2638

To compare the image quality of CT obtained using a deep learning-based image reconstruction (DLIR) engine with images with adaptive statistical iterative reconstruction-V (AV). Using a phantom, the noise power spectrum (NPS) and task-based transfer function (TTF) were measured in images with different reconstructions (filtered back projection [FBP], AV30, 50, 100, DLIR-L, M, H) at multiple doses. One hundred and twenty abdominal CTs with 30% dose reduction were processed using AV30, AV50, DLIR-L, M, H. Objective and subjective analyses were performed. The NPS peak of DLIR was lower than that of AV30 or AV50. Compared with AV30, the NPS average spatial frequencies were higher with DLIR-L or DLIR-M. For lower contrast objects, TTF in images with DLIR were higher than those with AV. The standard deviation in DLIR-H and DLIR-M was significantly lower than AV30 and AV50. The overall image quality was the best for DLIR-M ( ). DLIR showed improved image quality and decreased noise under a decreased radiation dose.