A study on application of deep learning reconstruction in low-dose brain CT in children with craniocerebral trauma
10.3760/cma.j.cn112149-20220222-00141
- VernacularTitle:深度学习重建在儿童脑外伤低剂量颅脑CT扫描中的应用研究
- Author:
Weian WEI
1
;
Ting YI
;
Qiuhong MA
;
Xiao DONG
;
Ke JIN
Author Information
1. 湖南省儿童医院放射科，长沙 410007
- Keywords:
Child;
Craniocerebral trauma;
Tomography, X-ray computed;
Deep learning reconstruction
- From:
Chinese Journal of Radiology
2022;56(11):1195-1201
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of deep learning reconstruction (DLR) in low-dose brain CT imaging in children with craniocerebral trauma.Methods:The CT data of 51 children with craniocerebral trauma complicated with intracerebral hemorrhage who received low dose brain CT were retrospectively collected in Hunan Children′s Hospital between June 2020 and February 2021. All images were reconstructed at 1.25 mm and 5 mm slice thickness utilizing two reconstruction algorithms and divided into six subgroups: ASIR-V with three different blending ratios (0, 50%, 100%), and DLR with three different reconstruction strengths [low (L), media (M) and high (H)]. The objective parameters including CT value, signal to noise ratio (SNR) and contrast to noise ratio (CNR) of dorsal thalamus (grey matter), white matter of frontal lobe and hemorrhagic lesion, as well as basicranial artifact noise (SD) and background SD were measured and calculated. Subjective evaluation was performed with a 5-point scale scoring. Objective parameters and subjective scores were compared among different groups using randomized block analysis of variance and Friedman test, respectively. The objective and subjective differences between 1.25 mm DLR-H and ASIR-V50% images were analyzed using paired samples t-test and correlated sample rank sum test. Results:The average CT dose index volume, dose length product and size-specific dose estimate of head CT were 17.7 (11.9, 21.1) mGy, 248.4 (142.2, 338.1) mGy·cm and (15.7±2.8) mGy. With the same thickness, the difference of CT values between the DLR and ASIR-V groups were stastistically significant ( P<0.05). The subjective scores of DLR groups were significantly better than those of ASIR-V; the higher was the reconstruction grade of ASIR-V and DLR, the higher SNR and CNR values and the lower SD value were obtained for each structure (all P<0.05). DLR images showed better objective parameters than ASIR-V50% images. Background:SD was lowest on DLR-H and ASIR-V100% images, with no significant difference found between these two groups. Using 1.25 mm thickness, DLR-H images showed higher SNR (for both gray matter and white matter) and CNR than ASIR-V100% images ( P<0.05). The subjective score was decreased with the slice thickness reduced. However, the average subjective scores of 1.25 mm DLR images were all over 3 points, while those of 1.25 mm ASIR-V images were less than 3 points, which could not fully meet the needs of diagnosis. Images of 1.25 mm DLR-H had higher background SD and artifact SD than 5 mm ASIR-V50% images ( t=2.96, 2.83, P=0.005, 0.007), while the score and other objective parameters were not statistically different between these two groups ( P>0.05). Conclusion:In children′s low-dose cerebral CT, DLR can improve image quality, with the DLR-H images displaying the highest image quality. It can also increase the SNR and CNR of gray and white matter of images with thin thickness.
