From the Centre de Recherche Cardio-Thoracique de Bordeaux, INSERM U1045, CIC 1401, Université Bordeaux Segalen, LaBRI, Mathematical Institute of Bordeaux (IMB), 146 rue Léo Saignat, 33076 Bordeaux, France (A.L., I.B., B.D.d.S., P.B., F.L., F.B., G.D.); CNRS, Bordeaux INP, LaBRI, UMR 5800, Bordeaux INP, UMR 5251, Talence, France (A.L., B.D.d.S., F.B.); CHU de Bordeaux, Service d'Imagerie Cardiovasculaire et Thoracique, Service des Maladies Respiratoires, Service d'Exploration Fonctionnelle Respiratoire, Centre de référence pédiatrique de la mucoviscidose, CIC 1401, Pessac, France (J.R., I.B., J.M., S.B., P.B., F.L., G.D.); INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France (I.B., P.B., F.L., G.D.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (T.B.); Department of Radiology, Grenoble-Alpes University Hospital, Grenoble, France (G.F.); Imaging Department, Hôpital La Timone, APHM, Aix Marseille University, Marseille, France (J.Y.G.); Department of Radiology, CHU Nantes, Nantes, France (R.L.); Department of Thoracic Imaging, Heart & Lung Institute, Lille, France (A.H.); and Pediatric Radiology Department, Clocheville Hospital, CHRU de Tours, Tours, France (B.M.).
Radiology. 2023 Jul;308(1):e230052. doi: 10.1148/radiol.230052.
Background Lung MRI with ultrashort echo times (UTEs) enables high-resolution and radiation-free morphologic imaging; however, its image quality is still lower than that of CT. Purpose To assess the image quality and clinical applicability of synthetic CT images generated from UTE MRI by a generative adversarial network (GAN). Materials and Methods This retrospective study included patients with cystic fibrosis (CF) who underwent both UTE MRI and CT on the same day at one of six institutions between January 2018 and December 2022. The two-dimensional GAN algorithm was trained using paired MRI and CT sections and tested, along with an external data set. Image quality was assessed quantitatively by measuring apparent contrast-to-noise ratio, apparent signal-to-noise ratio, and overall noise and qualitatively by using visual scores for features including artifacts. Two readers evaluated CF-related structural abnormalities and used them to determine clinical Bhalla scores. Results The training, test, and external data sets comprised 82 patients with CF (mean age, 21 years ± 11 [SD]; 42 male), 28 patients (mean age, 18 years ± 11; 16 male), and 46 patients (mean age, 20 years ± 11; 24 male), respectively. In the test data set, the contrast-to-noise ratio of synthetic CT images (median, 303 [IQR, 221-382]) was higher than that of UTE MRI scans (median, 9.3 [IQR, 6.6-35]; < .001). The median signal-to-noise ratio was similar between synthetic and real CT (88 [IQR, 84-92] vs 88 [IQR, 86-91]; = .96). Synthetic CT had a lower noise level than real CT (median score, 26 [IQR, 22-30] vs 42 [IQR, 32-50]; < .001) and the lowest level of artifacts (median score, 0 [IQR, 0-0]; < .001). The concordance between Bhalla scores for synthetic and real CT images was almost perfect (intraclass correlation coefficient, ≥0.92). Conclusion Synthetic CT images showed almost perfect concordance with real CT images for the depiction of CF-related pulmonary alterations and had better image quality than UTE MRI. Clinical trial registration no. NCT03357562 © RSNA, 2023 See also the editorial by Schiebler and Glide-Hurst in this issue.
背景 超短回波时间(UTE)肺部 MRI 可实现高分辨率且无辐射的形态学成像;然而,其图像质量仍低于 CT。目的 评估基于生成对抗网络(GAN)生成的 UTE MRI 合成 CT 图像的图像质量和临床适用性。材料与方法 本回顾性研究纳入了 6 家机构于 2018 年 1 月至 2022 年 12 月期间同一天接受 UTE MRI 和 CT 检查的囊性纤维化(CF)患者。二维 GAN 算法使用配对的 MRI 和 CT 切片进行训练和测试,并使用外部数据集进行测试。通过测量表观对比噪声比、表观信噪比和整体噪声来定量评估图像质量,并通过视觉评分评估特征(包括伪影)的定性图像质量。两位读者评估了 CF 相关的结构异常,并使用这些异常来确定临床 Bhalla 评分。结果 训练、测试和外部数据集分别包含 82 例 CF 患者(平均年龄 21 岁±11[标准差];42 例男性)、28 例患者(平均年龄 18 岁±11;16 例男性)和 46 例患者(平均年龄 20 岁±11;24 例男性)。在测试数据集中,合成 CT 图像的对比噪声比(中位数,303[四分位距,221-382])高于 UTE MRI 扫描(中位数,9.3[四分位距,6.6-35];<.001)。合成和真实 CT 的信号噪声比相似(中位数,88[四分位距,84-92]比 88[四分位距,86-91];=.96)。与真实 CT 相比,合成 CT 的噪声水平更低(中位数评分,26[四分位距,22-30]比 42[四分位距,32-50];<.001),且伪影水平最低(中位数评分,0[四分位距,0-0];<.001)。合成 CT 和真实 CT 图像的 Bhalla 评分之间具有近乎完美的一致性(组内相关系数,≥0.92)。结论 合成 CT 图像在显示 CF 相关的肺部改变方面与真实 CT 图像几乎完全一致,且图像质量优于 UTE MRI。临床试验注册号 NCT03357562 © RSNA,2023 本期杂志还刊登了 Schiebler 和 Glide-Hurst 的社论。