Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.).
Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.).
Acad Radiol. 2024 Jan;31(1):180-186. doi: 10.1016/j.acra.2023.05.010. Epub 2023 Jun 4.
Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIR) sequence of the brain compared to conventional (standard) FLAIR (FLAIR) imaging.
Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIR was conducted using the same MRI acquisition parameters as the FLAIR sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39 minute instead of 2:40 minutes (-38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed.
The average age of the patients was 63 ± 11years. FLAIR exhibited significantly less image noise than FLAIR, with P-values of< .001 and< .05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIR, with a median score of 4 compared to a median score of 3 in FLAIR (P-values of<.001 for both readers). In terms of overall image quality, FLAIR was rated superior to FLAIR, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIR in 68/70 cases.
The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation.
液体衰减反转恢复（FLAIR）成像在脑转移瘤的检测中发挥着越来越重要的作用，磁共振成像（MRI）检查的数量也随之增加。因此，本研究旨在探讨与常规（标准）FLAIR 成像相比，基于深度学习的新型加速 FLAIR （FLAIR）序列对脑图像质量和诊断信心的影响。
本研究回顾性纳入了 70 例连续接受脑 MRI 分期检查的患者。FLAIR 采用与 FLAIR 序列相同的 MRI 采集参数进行，除并行成像的加速因子更高（从 2 增加到 4）外，采集时间从 2:40 分钟缩短至 1:39 分钟（缩短 38%）。两名专业神经放射科医生使用 1 到 4 的李克特量表对成像数据集进行评估，4 表示以下参数的最佳评分：清晰度、病灶边界、伪影、整体图像质量和诊断信心。此外，还评估了读者对图像的偏好和读者间的一致性。
患者的平均年龄为 63 ± 11 岁。FLAIR 的图像噪声明显低于 FLAIR，P 值均<0.001 和<0.05。FLAIR 的图像清晰度和检测病灶的能力更高，两位医生的中位数评分均为 4，而 FLAIR 的中位数评分为 3（均为 P<0.001）。在整体图像质量方面，FLAIR 的评分也优于 FLAIR，两位医生的中位数评分均为 4 对 3（均为 P<0.001）。在 70 例病例中，两位医生均更倾向于使用 FLAIR。
与标准 FLAIR 成像相比，深度学习 FLAIR 脑成像技术在检查时间上可缩短 38%，且具有更好的图像质量、噪声降低和病灶边界清晰的优势。
