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

Yeo Jin Yoo; In Young Choi; Suk Keu Yeom; Sang Hoon Cha; Yunsub Jung; Hyun Jong Han; Euddeum Shim

doi:10.5334/jbsr.2638

Abstract

Purpose: 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).

Materials and Methods: 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.

Results: 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 (p < 0.001).

Conclusions: DLIR showed improved image quality and decreased noise under a decreased radiation dose.

References

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

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