Abstract
BackgroundTo evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images.MethodsLow-dose axial head CT scans of 50 children with 120 kV, 0.8 s rotation and age-dependent 150–220 mA tube current were selected. Images were reconstructed at 5 mm and 0.625 mm slice thickness using Filtered back projection (FBP), Adaptive statistical iterative reconstruction-v at 50% strength (50%ASIR-V) (as reference standard), 100%ASIR-V and DLIR-high (DL-H). The CT attenuation and standard deviation values of the gray and white matters in the basal ganglia were measured. The clarity of sulci/cisterns, boundary between white and gray matters, and overall image quality was subjectively evaluated. The number of lesions in each reconstruction group was counted.ResultsThe 5 mm FBP, 50%ASIR-V, 100%ASIR-V and DL-H images had a subjective score of 2.25 ± 0.44, 3.05 ± 0.23, 2.87 ± 0.39 and 3.64 ± 0.49 in a 5-point scale, respectively with DL-H having the lowest image noise of white matter at 2.00 ± 0.34 HU; For the 0.625 mm images, only DL-H images met the diagnostic requirement. The 0.625 mm DL-H images had similar image noise (3.11 ± 0.58 HU) of the white matter and overall image quality score (3.04 ± 0.33) as the 5 mm 50% ASIR-V images (3.16 ± 0.60 HU and 3.05 ± 0.23). Sixty-five lesions were recognized in 5 mm 50%ASIR-V images and 69 were detected in 0.625 mm DL-H images.ConclusionDL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625 mm DL-H images improve lesion detection and produce similar image noise as the 5 mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.
Highlights
To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images
In our study, we evaluated a deep learning image reconstruction (DLIR) algorithm in its ability to significantly improve image quality at the same slice thickness and maintain similar image noise at much thinner slice thickness for potential dose reduction as the state-of-the-art 50%Adaptive statistical iterative reconstruction-v (ASIR-V) algorithm and demonstrated the improved lesion detection with thinner slice in head CT
CT is commonly used in pediatric emergency care, and the head CT is often the first choice for skull fracture, deformation and trauma complicated with hemorrhage [1, 6, 16]
Summary
To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images. The DLIR engine builds upon specific knowledge of the detailed design of the particular CT system which includes knowledge of the conditioning of the collected data Even more importantly, this knowledge is embedded within a DNN, which is capable of learning through a large number of real-world examples. The objective of our paper was to retrospectively review a series of head CT images reconstructed using the newly developed DLIR algorithm from children who received emergency care and to evaluate whether the application of this DLIR algorithm could further improve the image quality and make it possible to further reduce the dose of head CT for children or significantly improve spatial resolution and lesion detectability while maintaining low radiation dose, in comparison with the state-of-the-art adaptive statistical iterative reconstruction (ASIR-V, GE Healthcare, Waukesha USA) algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
