Application of Deep Learning-Based Image Reconstruction Technology in 5.0T MRI for Nasopharyngeal Carcinoma
10.3969/j.issn.1005-5185.2025.07.002
- VernacularTitle:基于深度学习的图像重建技术在鼻咽癌5.0T MRI中的应用
- Author:
Penghui ZHOU
1
;
Haibin LIU
;
Hai LIN
;
Ziming YU
;
Guixiao XU
;
Haoqiang HE
;
Chuanmiao XIE
Author Information
1. 中山大学肿瘤防治中心影像科,广东 广州 510060
- Publication Type:Journal Article
- Keywords:
Nasopharyngeal carcinoma;
Magnetic resonance imaging;
Ultra-high field;
Artificial intelligence;
Deep learning;
Image quality
- From:
Chinese Journal of Medical Imaging
2025;33(7):694-699
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To explore the feasibility and clinical value of deep learning-based image reconstruction technology in 5.0T MRI for nasopharyngeal carcinoma.Materials and Methods A prospective study was conducted on 50 newly diagnosed nasopharyngeal carcinoma patients from August to December 2024 at Sun Yat-sen University Cancer Center.5.0T MRI was performed to scan the nasopharynx region.Routine scanning protocols included transverse T2WI,transverse T1WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI sequences.Based on these standard scanning protocols,DeepRecon deep learning reconstruction technology with different levels(grade 1-5)was applied,generating a total of 24 sets of images.Qualitative evaluation employed a Likert scale(5-point system)for subjective scoring on lesion detection,lesion edge clarity,artifacts and overall image quality.Quantitative evaluation was performed using the signal-to-noise ratio and contrast-to-noise ratio to objectively assess the quality of the 24 image sets.Differences in qualitative and quantitative indicators between different groups were compared,while the Kappa coefficient was used to analyze the consistency of subjective evaluations by two radiologists.Results In the qualitative assessment of 24 image sets from four MRI sequences(with and without DeepRecon reconstruction),DeepRecon images(grade 2-4)significantly outperformed traditional images in all features except for artifact reduction(Z=-12.11--6.23,all P<0.001).Images reconstructed at DeepRecon grade 3 had the highest overall score and the best image quality.Furthermore,compared with traditional images,DeepRecon images(grade 2-5)demonstrated significantly improved signal-to-noise ratio for both lesions and the lateral pterygoid muscle(t=-15.67--3.44,Z=-6.09--4.63,all P<0.01).In addition,in the transverse T2WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI images with DeepRecon reconstruction(grade 2-5),the contrast-to-noise ratio(lesion/lateral pterygoid muscle)also showed significant improvement compared to traditional images(t=-12.71--3.19,Z=-6.08--4.47,all P<0.001).The inter-observer agreement for the overall subjective quality score between the two radiologists was good(Kappa=0.75-0.82,all P<0.01).Conclusion DeepRecon deep learning reconstruction technology significantly increases the signal-to-noise ratio and resolution of traditional magnetic resonance images of nasopharyngeal cancer,improving image clarity and bringing more possibilities for the advancement of imaging diagnosis.
