Abstract
BackgroundEfforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.MethodsThis retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.ResultsDLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture.ConclusionCompared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.
Highlights
Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques
The iterative reconstruction (IR) algorithm has been used to produce high-resolution images by decreasing image noise through the use of computational processing, resulting in better image quality with lower radiation dose compared with single reconstructed filtered back projection (FBP) in adults [1, 2] and children [3,4,5,6]
When compared with 50% adaptive statistical iterative reconstruction-V (ASIR-V), high strength deep learning reconstruction (DLR) was associated with noise reduction in non-contrast chest computed tomography (CT) (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in contrast to noise ratio (CNR) at 149.1%, 105.8%, and 53.1%, respectively, and increases in Signal to noise (SNR) at 148.6%, 106.3%, and 57.4%, respectively (Fig. 2, Additional file 1: Fig. S1–S4)
Summary
Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. With the development of technology, efforts to reduce the radiation dose have continued steadily, with the development and use of iterative reconstruction (IR) as a typical example. The IR algorithm has been used to produce high-resolution images by decreasing image noise through the use of computational processing, resulting in better image quality with lower radiation dose compared with single reconstructed filtered back projection (FBP) in adults [1, 2] and children [3,4,5,6]. The recently developed adaptive statistical iterative reconstruction-V (ASIR-V) technique provides a short reconstruction time with better image quality and lowers radiation dose than other IR algorithms [7, 8]. Hybrid IR images that blend IR with FBP can be used to decrease this texture problem, a trade-off between image noise and image texture occurs [9]
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