Gordaliza Pedro M, Müller Jannis, Cagol Alessandro, Molchanova Nataliia, La Rosa Francesco, Tsagkas Charidimos, Granziera Cristina, Cuadra Meritxell Bach
CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland.
Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, Faculty of Medicine, Basel, Switzerland; University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, MS Center University Hospital, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), Basel, Switzerland.
Neuroimage Clin. 2025 Jul 14;48:103818. doi: 10.1016/j.nicl.2025.103818.
To investigate the efficacy of Fluid and White Matter Suppression (FLAWS) MRI sequence in improving Deep Learning (DL)-based detection and segmentation of cortical lesions in Multiple Sclerosis (MS) patients even, and to develop models that can generalize to clinical settings where only standard T1-weighted images (MPRAGE) are available.
In this multi-site study, we analyzed 204 MS patients using DL models developed with FLAWS and Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequences. Reference standard annotations were established through two approaches: (1) consensus of three expert raters across all contrasts, and (2) single-rater annotations for individual modalities. Models were validated on both internal and external datasets, with performance assessed using F-score for detection and DSC for segmentation accuracy.
Models involving FLAWS demonstrated superior performance over MP2RAGE-only models. The model combining MP2RAGE and FLAWS achieved CL detection with median F-score of 0.667[0.339-0.840] compared to multi-rater consensus. Models trained on comprehensive consensus annotations outperformed those trained on single-modality annotations. Notably, a model trained on MP2RAGE but leveraging FLAWS-derived annotations showed strong generalization when applied to standard clinical Magnetization Prepared Rapid Gradient-Echo (MPRAGE) datasets from a different institution (median F-score: 0.55[0.211-0.998]), demonstrating successful knowledge transfer from advanced research sequences to routine clinical sequences.
Integration of FLAWS-derived contrasts and annotations significantly improves DL-based CL detection and segmentation. The models demonstrate capability in identifying lesions missed by individual raters and maintain robust performance when applied to standard clinical sequences at external sites. This cross-sequence generalization facilitates immediate clinical translation, supported by publicly available inference models on DockerHub.
研究液体与白质抑制（FLAWS）磁共振成像序列在改善基于深度学习（DL）的多发性硬化症（MS）患者皮质病变检测和分割方面的效果，甚至开发能够推广到仅提供标准T1加权图像（MPRAGE）的临床环境中的模型。
在这项多中心研究中，我们使用基于FLAWS序列和磁化准备快速采集梯度回波（MP2RAGE）序列开发的DL模型分析了204例MS患者。通过两种方法建立参考标准注释：（1）所有对比序列的三位专家评分者的共识，以及（2）单个模态的单评分者注释。模型在内部和外部数据集上均进行了验证，使用F分数评估检测性能，使用Dice相似系数（DSC）评估分割准确性。
涉及FLAWS的模型表现优于仅使用MP2RAGE的模型。与多评分者共识相比，结合MP2RAGE和FLAWS的模型实现了皮质病变（CL）检测，中位数F分数为0.667[0.339 - 0.840]。在综合共识注释上训练的模型优于在单模态注释上训练的模型。值得注意的是，一个在MP2RAGE上训练但利用FLAWS衍生注释的模型，在应用于来自不同机构的标准临床磁化准备快速梯度回波（MPRAGE）数据集时表现出很强的泛化能力（中位数F分数：0.55[0.211 - 0.998]），证明了从先进研究序列到常规临床序列的成功知识转移。
FLAWS衍生的对比和注释的整合显著改善了基于DL的CL检测和分割。这些模型展示了识别单个评分者遗漏病变的能力，并且在应用于外部站点的标准临床序列时保持稳健的性能。这种跨序列泛化促进了即时临床转化，由DockerHub上公开可用的推理模型提供支持。
