Deep learning reconstruction algorithm for improving imaging quality of accelerated T2WI of cervical tumors
10.13929/j.issn.1003-3289.2025.09.025
- VernacularTitle:深度学习重建算法用于改善颈部肿瘤加速T2WI成像质量
- Author:
Yun WANG
1
;
Tianjiao WANG
1
;
Yu CHEN
1
;
Tong SU
1
;
Feng FENG
1
;
Zhengyu JIN
1
Author Information
1. 中国医学科学院北京协和医学院北京协和医院放射科,北京 100730
- Publication Type:Journal Article
- Keywords:
head and neck neoplasms;
deep learning;
magnetic resonance imaging
- From:
Chinese Journal of Medical Imaging Technology
2025;41(9):1573-1576
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of deep learning(DL)reconstruction algorithm for improving imaging quality of accelerated T2WI of cervical tumors.Methods A total of 43 patients with suspected cervical tumors were prospectively enrolled.Cervical conventional T2WI and accelerated T2WI based on DL reconstruction(DL-T2WI)were acquired.The imaging quality was subjectively assessed by 2 physicians using a 4-point system,including overall image quality,artifact,noise,sharpness and lesion detectability scores,and then were compared between conventional T2WI and DL-T2WI.Results The acquisition of conventional T2WI took 116 s,while of DL-T2WI took 101 s.The inter-observer consistency of subjective evaluation results on the overall image quality,artifact,noise,sharpness and lesion detectability scores were all excellent(Kappa=0.851-0.969).No significant difference of subjective evaluation results on overall image quality nor lesion detectability scores was found between conventional T 2WI and DL-T2WI(both P＞0.05),while the artifact and sharpness scores of DL-T2WI were significantly higher but the noise score was significantly lower than those of conventional T2WI(all P＜0.05).Conclusion DL reconstruction algorithm was helpful for improving imaging quality of accelerated T 2WI of cervical tumors.
