南京医科大学学报（自然科学版）2025，Vol.45Issue(6)：810-815,825,7.DOI:10.7655/NYDXBNSN241084
深度学习重建算法在胰腺HASTE-T2WI序列中的临床应用价值
The clinical value of deep learning reconstruction algorithm in pancreatic HASTE-T2WI sequence
摘要
Abstract
Objective:To evaluate the clinical value of deep learning(DL)-based reconstruction algorithm for half-Fourier acquisition single-shot turbo spin echo(HASTE)T2-weighted imaging(T2WI)in pancreatic magnetic resonance imaging(MRI).Methods:A total of 69 patients underwent pancreatic conventional BLADE-TSE-T2WI and based on deep learning HASTE-DL-T2WI sequences scanning using 3.0T MR.The overall image quality,pancreatic sharpness,biliary duct clarity,and artifacts were subjectively scored using a Likert 5-point scale.The contrast to noise ratio(CNR)and signal to noise ratio(SNR)of normal pancreatic tissue and the lesion in both sequences were measured and compared,and the scan time was recorded.Results:The HASTE-DL sequence scored significantly higher in overall image quality,pancreatic sharpness,and bile duct clarity than the BLADE-TSE sequence(P<0.001),with no statistical difference in artifact scores(P>0.05).The SNR of normal pancreatic tissue,lesion SNR,and CNR in HASTE-DL images were superior to those of BLADE-TSE sequence(P<0.001).Additionally,the scanning time of HASTE-DL was reduced by 78%compared to BLADE-TSE.Conclusion:Compared to BLADE-TSE sequence,HASTE-DL provides better overall image quality,superior pancreatic sharpness and bile duct clarity,higher SNR and CNR,and significantly shorter scan time.Thus,HASTE-DL T2WI demonstrates excellent clinical utility in pancreatic MRI.关键词
胰腺/磁共振成像/图像质量/深度学习Key words
pancreas/magnetic resonance imaging/image quality/deep learning分类
临床医学引用本文复制引用
徐思雨,张永杰,田水,王建伟..深度学习重建算法在胰腺HASTE-T2WI序列中的临床应用价值[J].南京医科大学学报（自然科学版）,2025,45(6):810-815,825,7.基金项目
江苏省青蓝工程中青年学术带头人基金(KY101R202023) （KY101R202023）
