基于深度学习的图像重建技术在 1.5 T 下加速肝脏弥散加权磁共振成像:对图像质量和病灶检测的影响。

IF 1 4区 医学 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Luke A Ginocchio, Sonam Jaglan, Angela Tong, Paul N Smereka, Thomas Benkert, Hersh Chandarana, Krishna P Shanbhogue
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引用次数: 0

摘要

目的比较基于深度学习的多波段扩散加权序列(DL-mb-DWI)、加速多波段扩散加权序列(加速mb-DWI)和传统多波段扩散加权序列(传统mb-DWI)在临床肝脏磁共振成像(MRI)患者中的图像质量:本研究纳入了 2021 年 9 月 1 日至 2022 年 1 月 31 日期间在 1.5 T 扫描仪上接受肝脏临床磁共振成像的 50 名连续患者。三位放射科医生采用5点Likert评分法对图像进行独立审查,除了评估是否存在肝脏病变和病变的清晰度外,还对伪影和图像质量因素进行审查:DL-mb-DWI采集时间为65.0±2.4秒,明显(P<0.001)短于传统mb-DWI(147.5±19.2秒)和加速mb-DWI(94.3±1.8秒)。在左叶清晰度(P < 0.001)、肝内血管边缘锐利度(P < 0.001)、胰腺轮廓锐利度(P < 0.001)、平面内运动伪影(P = 0.002)和整体图像质量(P = 0.005)方面,读者 2 的 DL-mb-DWI 得分明显高于传统 mb-DWI。读者 3 对 DL-mb-DWI 左叶的清晰度(P = 0.006)、胰腺轮廓的清晰度(P = 0.020)和平面内运动伪影(P = 0.042)的评分明显更高。在脂肪抑制强度(P = 0.004)和胰腺轮廓清晰度(P = 0.038)方面,读者 1 的 DL-mb-DWI 得分明显更高。其余质量参数对读者 1 来说没有统计学意义:与传统和加速mb-DWI序列相比,基于深度学习图像重建的新型弥散加权磁共振成像序列显著缩短了采集时间,同时保持或提高了常规腹部磁共振成像的图像质量。在常规临床肝脏 MRI 中,DL-mb-DWI 有可能替代传统的 mb-DWI。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerated Diffusion-Weighted Magnetic Resonance Imaging of the Liver at 1.5 T With Deep Learning-Based Image Reconstruction: Impact on Image Quality and Lesion Detection.

Objective: To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI).

Methods: Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study. Three radiologists independently reviewed images using a 5-point Likert scale for artifacts and image quality factors, in addition to assessing the presence of liver lesions and lesion conspicuity.

Results: DL-mb-DWI acquisition time was 65.0 ± 2.4 seconds, significantly (P < 0.001) shorter than conventional mb-DWI (147.5 ± 19.2 seconds) and accelerated mb-DWI (94.3 ± 1.8 seconds). DL-mb-DWI received significantly higher scores than conventional mb-DWI for conspicuity of the left lobe (P < 0.001), sharpness of intrahepatic vessel margin (P < 0.001), sharpness of the pancreatic contour (P < 0.001), in-plane motion artifact (P = 0.002), and overall image quality (P = 0.005) by reader 2. DL-mb-DWI received significantly higher scores for conspicuity of the left lobe (P = 0.006), sharpness of the pancreatic contour (P = 0.020), and in-plane motion artifact (P = 0.042) by reader 3. DL-mb-DWI received significantly higher scores for strength of fat suppression (P = 0.004) and sharpness of the pancreatic contour (P = 0.038) by reader 1. The remaining quality parameters did not reach statistical significance for reader 1.

Conclusions: Novel diffusion-weighted MRI sequence with deep learning-based image reconstruction demonstrated significantly decreased acquisition times compared with conventional and accelerated mb-DWI sequences, while maintaining or improving image quality for routine abdominal MRI. DL-mb-DWI offers a potential alternative to conventional mb-DWI in routine clinical liver MRI.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).
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