诊断学理论与实践

诊断学理论与实践 >

2025 , Vol. 24 >Issue 06: 628 - 633

DOI: https://doi.org/10.16150/j.1671-2870.2025.06.008

论著

基于深度学习的快速MRCP成像在屏气困难患者检查中的应用价值

  • 孔德艳 ,
  • 高祎阳 ,
  • 张曹阳 ,
  • 董海鹏 ,
  • 严福华 ,
  • 原子扬 ,
  • 代建琨 ,
  • 刘方韬
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  • 1.上海交通大学医学院附属瑞金医院放射科， 上海 200025
    2.通用电气医疗系统贸易发展（上海）有限公司， 上海 200120
刘方韬 E-mail：lft40620@rjh.com.cn

收稿日期: 2025-03-24

  修回日期: 2025-07-25

  网络出版日期: 2025-12-25

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Application value of deep learning-based rapid MRCP imaging in examination of patients with breath holding difficulties

  • KONG Deyan ,
  • GAO Yiyang ,
  • Zhang Caoyang ,
  • DONG Haipeng ,
  • YAN Fuhua ,
  • YUAN Ziyang ,
  • DAI Jiankun ,
  • LIU Fangtao
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  • 1. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
    2. General Electric Healthcare Systems Trade Development (Shanghai) Company, Shanghai 200120, China

Received date: 2025-03-24

  Revised date: 2025-07-25

  Online published: 2025-12-25

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摘要

目的： 比较基于快速深度学习（deep learning speed，DLS）的三维屏气（breathe hold，BH）磁共振胰胆管成像（magnetic resonance cholangiopancreatography ，MRCP）与常规的三维屏气磁共振胰胆管成像（3D BH MRCP）在无法配合屏气的情况下的图像质量。方法： 前瞻性纳入2025年1月至3月在上海交通大学医学院附属瑞金医院50例屏气配合不佳MRCP检查的患者，先后进行BH 3D DLS MRCP、BH 3D MRCP序列的扫描。比较2个序列的扫描时间，客观评价2组图像的信噪比（signal to noise ratio， SNR）、对比噪声比（contrast to noise ratio，CNR），主观评价2组图像的整体图像质量、背景抑制、胰胆管可视化质量。结果： 3D BH DLS MRCP组平均扫描时间为11 s，3D BH MRCP组平均扫描时间为17 s，3D BH DLS MRCP扫描时间缩短（P<0.05）。客观评价显示3D BH DLS MRCP的SNR（23.76±9.23）和CNR（28.89±11.49）均好于3D BH MRCP[（17.15±6.85）、（22.03±8.79）]（P<0.05）；主观评价图像分析显示，3D BH DLS MRCP在图像整体质量评分[（3.80±0.32）分]、背景抑制[（3.64±0.40）分]及显示胰胆管各段[（3.53±0.37）分）]均有高于3D BH MRCP的图像整体质量[（3.10±0.44）分]、背景抑制[（3.33±0.51分]）及显示胰胆管各段[（3.08±0.56）分]的趋势，但差异尚无统计学意义。3D BH DLS MRCP组50例均满足诊断要求，3D BH MRCP组有5例不满足诊断要求，2组间差异有统计学意义（P<0.05）。结论： 在无法屏气或屏气不佳的情况下，应用3D BH DLS MRCP检查可显著缩短检查时间，图像质量有保证，符合临床诊断需求。

关键词： 磁共振胰胆管成像; 快速深度学习; 屏气

本文引用格式

孔德艳 , 高祎阳 , 张曹阳 , 董海鹏 , 严福华 , 原子扬 , 代建琨 , 刘方韬 . 基于深度学习的快速MRCP成像在屏气困难患者检查中的应用价值[J]. 诊断学理论与实践, 2025 , 24(06) : 628 -633 . DOI: 10.16150/j.1671-2870.2025.06.008

Abstract

Objective To compare the image quality of rapid deep learning (RDL)-based three-dimensional (3D) breath holding (BH) magnetic resonance cholangiopancreatography (MRCP) and that of conventional 3D BH MRCP in patients unable to cooperate with breath holding. Methods A total of 50 patients with poor breath holding cooperation who underwent MRCP examination at Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine from January to March 2025 were prospectively enrolled for scanning with 3D BH DLS MRCP(DLS group) and BH 3D MRCP(MRCP group) sequences successively. The scanning duration of the two sequences were compared. Objective analysis including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images, as well as subjective analysis including the overall image quality, background suppression, and visualization of the pancreatic and biliary ducts in both groups were compared. Results The averge scanning duration was 11 s for the 3D BH DLS MRCP group and 17 s for the 3D BH MRCP group, indicating a significant reduction in scanning time for 3D BH DLS MRCP (P<0.05). Objective analysis revealed that both the SNR (23.76±9.23) and CNR (28.89±11.49) of 3D BH DLS MRCP were higher than those of 3D BH MRCP [(17.15±6.85), (22.03±8.79)] (P<0.05). Subjective image analysis showed that 3D BH DLS MRCP outperformed 3D BH MRCP in overall image quality scores (3.80±0.32 vs 3.10±0.44), background suppression (3.64±0.40 vs 3.33±0.51), and visualization of pancreaticobiliary duct segments (3.53±0.37 vs 3.08±0.56) with an upward trend(P>0.05). Results of all 50 cases in the 3D BH DLS MRCP sequence met the diagnostic requirements, while of 5 cases in the 3D BH MRCP sequence did not, with a statistically significant difference between the two sequences (P<0.05). Conclusions In cases where breath holding is impossible or poorly performed, the use of 3D BH DLS MRCP could significantly shortens examination time, with image quality of the same level as BH 3D MRCP, and meets clinical diagnostic requirements.

Key words： Magnetic resonance cholangiopancreatography; Rapid deep learning; Breath holding

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