Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

Hyoung Suk Park,Kiwan Jeon,Jeongeun Lee,Sun Kyoung You,Jie Zhang

doi:10.1371/journal.pone.0260369

Abstract

ObjectivesTo evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.Materials and methodsLDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set.ResultsThe noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images.ConclusionThe deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.

Highlights

Demand for radiation dose reduction is growing as the use of computed tomography (CT) for pediatric patients increases [1, 2]
The deep learning method trained by unpaired datasets could reduce noise of low-dose CT (LDCT) images and showed similar performance to SAFIRE
It can be applied to LDCT images of older CT scanners without iterative reconstruction (IR)

Summary

Introduction

Demand for radiation dose reduction is growing as the use of computed tomography (CT) for pediatric patients increases [1, 2]. With recent advances in CT technology, various commercial iterative reconstruction (IR) methods have been proposed and have demonstrated the potential to improve the quality of the images reconstructed from low-dose scans [4,5,6]. A type of machine learning [7], has been recently proposed for CT dose reduction and has shown the potential to reduce noise artifacts [8,9,10]. Most of these approaches are based on learning the relationship between LDCT images and standard-dose CT (SDCT) images by using a pair of low-dose and high-dose CT images. Obtaining two scans with low-dose and standard-dose protocols simultaneously is often not feasible in real medical imaging practices

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