Liebert Andrzej, Hadler Dominique, Ehring Chris, Schreiter Hannes, Brock Luise, Kapsner Lorenz A, Eberle Jessica, Erber Ramona, Emons Julius, Laun Frederik B, Uder Michael, Wenkel Evelyn, Ohlmeyer Sabine, Bickelhaupt Sebastian
Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Eur Radiol Exp. 2025 May 2;9(1):47. doi: 10.1186/s41747-025-00580-3.
Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neural network can generate virtual T2w-FS (VirtuT2w) images from routine multiparametric breast MRI images.
This IRB-approved, retrospective study included 914 breast MRI examinations from January 2017 to June 2020. The dataset was divided into training (n = 665), validation (n = 74), and test sets (n = 175). The U-Net was trained using different input protocols consisting of T1-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences to generate VirtuT2. Quantitative metrics were used to evaluate the different input protocols. A qualitative assessment by two radiologists was used to evaluate the VirtuT2w images of the best input protocol.
VirtuT2w images demonstrated the best quantitative metrics compared to original T2w-FS images for an input protocol using all of the available data. A high level of high-frequency error norm (0.87) indicated a strong blurring presence in the VirtuT2 images, which was also confirmed by qualitative reading. Radiologists correctly identified VirtuT2 images with at least 96% accuracy. Significant difference in diagnostic image quality was noted for both readers (p ≤ 0.015). Moderate inter-reader agreement was observed for edema detection on both T2w-FS images (κ = 0.49) and VirtuT2 images (κ = 0.44).
The 2D-U-Net generated virtual T2w-FS images similar to real T2w-FS images, though blurring remains a limitation. Investigation of other architectures and using larger datasets is necessary to improve potential future clinical applicability.
Generating VirtuT2 images could potentially decrease the examination time of multiparametric breast MRI, but its quality needs to improve before introduction into a clinical setting.
Breast MRI T2w-fat-saturated (FS) images can be virtually generated using convolutional neural networks. Image blurring in virtual T2w-FS images currently limits their clinical applicability. Best quantitative performance could be achieved when using full dynamic-contrast-enhanced acquisition and DWI as input of the neural network.
乳腺磁共振成像（MRI）方案通常包括T2加权脂肪抑制（T2w-FS）序列，该序列有助于组织特征分析，但会显著增加扫描时间。本研究旨在评估二维U-Net神经网络能否从常规多参数乳腺MRI图像生成虚拟T2w-FS（VirtuT2w）图像。
这项经机构审查委员会批准的回顾性研究纳入了2017年1月至2020年6月期间的914例乳腺MRI检查。数据集分为训练集（n = 665）、验证集（n = 74）和测试集（n = 175）。使用由T1加权、扩散加权和动态对比增强序列组成的不同输入方案训练U-Net以生成VirtuT2。使用定量指标评估不同的输入方案。由两名放射科医生进行定性评估，以评估最佳输入方案的VirtuT2w图像。
对于使用所有可用数据的输入方案，与原始T2w-FS图像相比，VirtuT2w图像显示出最佳的定量指标。较高的高频误差范数（0.87）表明VirtuT2图像中存在明显模糊，定性读片也证实了这一点。放射科医生识别VirtuT2图像的准确率至少为96%。两位读者在诊断图像质量上均存在显著差异（p≤0.015）。在T2w-FS图像（κ = 0.49）和VirtuT2图像（κ = 0.44）上，读者间在水肿检测方面的一致性为中等。
二维U-Net生成的虚拟T2w-FS图像与真实T2w-FS图像相似，尽管模糊仍是一个限制因素。有必要研究其他架构并使用更大的数据集，以提高未来潜在的临床适用性。
生成VirtuT2图像可能会减少多参数乳腺MRI的检查时间，但在引入临床应用之前，其质量需要提高。
乳腺MRI的T2w脂肪抑制（FS）图像可使用卷积神经网络虚拟生成。虚拟T2w-FS图像中的图像模糊目前限制了它们的临床应用。当使用全动态对比增强采集和扩散加权成像作为神经网络的输入时，可实现最佳的定量性能。
