Pelcat Antoine, Le Berre Alice, Ben Hassen Wagih, Debacker Clement, Charron Sylvain, Thirion Bertrand, Legrand Laurence, Turc Guillaume, Oppenheim Catherine, Benzakoun Joseph
Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, IMA-BRAIN, 75014 Paris, France.
Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, IMA-BRAIN, 75014 Paris, France; GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, Department of Neuroradiology, 75014 Paris, France.
Diagn Interv Imaging. 2025 Jul-Aug;106(7-8):264-271. doi: 10.1016/j.diii.2025.03.004. Epub 2025 Mar 19.
The purpose of this study was to validate a deep learning algorithm that generates T2*-weighted images from diffusion-weighted (DW) images and to compare its performance with that of true T2*-weighted images for hemorrhage detection on MRI in patients with acute stroke.
This single-center, retrospective study included DW- and T2*-weighted images obtained less than 48 hours after symptom onset in consecutive patients admitted for acute stroke. Datasets were divided into training (60 %), validation (20 %), and test (20 %) sets, with stratification by stroke type (hemorrhagic/ischemic). A generative adversarial network was trained to produce generative T2*-weighted images using DW images. Concordance between true T2*-weighted images and generative T2*-weighted images for hemorrhage detection was independently graded by two readers into three categories (parenchymal hematoma, hemorrhagic infarct or no hemorrhage), and discordances were resolved by consensus reading. Sensitivity, specificity and accuracy of generative T2*-weighted images were estimated using true T2*-weighted images as the standard of reference.
A total of 1491 MRI sets from 939 patients (487 women, 452 men) with a median age of 71 years (first quartile, 57; third quartile, 81; range: 21-101) were included. In the test set (n = 300), there were no differences between true T2*-weighted images and generative T2*-weighted images for intraobserver reproducibility (κ = 0.97 [95 % CI: 0.95-0.99] vs. 0.95 [95 % CI: 0.92-0.97]; P = 0.27) and interobserver reproducibility (κ = 0.93 [95 % CI: 0.90-0.97] vs. 0.92 [95 % CI: 0.88-0.96]; P = 0.64). After consensus reading, concordance between true T2*-weighted images and generative T2*-weighted images was excellent (κ = 0.92; 95 % CI: 0.91-0.96). Generative T2*-weighted images achieved 90 % sensitivity (73/81; 95 % CI: 81-96), 97 % specificity (213/219; 95 % CI: 94-99) and 95 % accuracy (286/300; 95 % CI: 92-97) for the diagnosis of any cerebral hemorrhage (hemorrhagic infarct or parenchymal hemorrhage).
Generative T2*-weighted images and true T2*-weighted images have non-different diagnostic performances for hemorrhage detection in patients with acute stroke and may be used to shorten MRI protocols.
本研究旨在验证一种能从扩散加权(DW)图像生成T2加权图像的深度学习算法,并将其在急性卒中患者MRI上检测出血的性能与真实T2加权图像进行比较。
这项单中心回顾性研究纳入了因急性卒中入院的连续患者在症状发作后48小时内获取的DW图像和T2加权图像。数据集分为训练集(60%)、验证集(20%)和测试集(20%),并按卒中类型(出血性/缺血性)分层。训练一个生成对抗网络,使用DW图像生成生成性T2加权图像。由两名阅片者独立将真实T2加权图像和生成性T2加权图像在检测出血方面的一致性分为三类(实质血肿、出血性梗死或无出血),不一致情况通过共同阅片解决。以真实T2加权图像作为参考标准,评估生成性T2加权图像的敏感性、特异性和准确性。
共纳入939例患者(487例女性,452例男性)的1491套MRI检查,中位年龄71岁(第一四分位数,57岁;第三四分位数,81岁;范围:21 - 101岁)。在测试集(n = 300)中,真实T2加权图像和生成性T2加权图像在观察者内重复性方面无差异(κ = 0.97 [95%CI:0.95 - 0.99] 对 0.95 [95%CI:0.92 - 0.97];P = 0.27),在观察者间重复性方面也无差异(κ = 0.93 [95%CI:0.90 - 0.97] 对 0.92 [95%CI:0.88 - 0.96];P = 0.64)。经过共同阅片后,真实T2加权图像和生成性T2加权图像之间的一致性极佳(κ = 0.92;95%CI:0.91 - 0.96)。生成性T2*加权图像在诊断任何脑出血(出血性梗死或实质出血)方面的敏感性为90%(73/81;95%CI:81 - 96),特异性为97%(213/219;95%CI:94 - 99),准确性为95%(286/300;95%CI:92 - 97)。
生成性T加权图像与真实T2加权图像在急性卒中患者出血检测方面具有相同的诊断性能,可用于缩短MRI检查流程。