标题： 实时、在线的定量MRI，通过扫描仪集成的机器学习：NODDI的原理验证 显示英文标题

标题： Real-time, inline quantitative MRI enabled by scanner-integrated machine learning: a proof of principle with NODDI

摘要： 目的：许多先进的定量磁共振成像（qMRI）技术的临床可行性及其转化受到限制，因为它们仅限于“研究模式”，这是由于资源密集型、离线参数估计所致。本研究旨在通过使用训练好的神经网络（NN）实现实时、内联参数估计，并将其完全集成到供应商的图像重建环境中，从而促进和鼓励先进qMRI技术的临床应用。 方法：对西门子图像计算环境（ICE）流程进行了定制，以使用开放神经网络交换（ONNX）运行时部署训练好的NN用于高级扩散MRI参数估计。两个全连接的NN使用通过神经元方向分散和密度成像（NODDI）模型合成的数据进行离线训练，使用传统估计（NNMLE）或真实值（NNGT）参数作为训练标签。该策略通过体内采集进行了在线演示，并通过合成测试数据进行了离线评估。 结果：NN成功地原生集成并部署在ICE中，在<10秒内实现了整个大脑的体内NODDI参数估计。从扫描仪导出DICOM参数图用于进一步分析，通常发现NNMLE估计比NNGT更一致。离线评估确认NNMLE与传统拟合具有可比的准确性，并且在噪声鲁棒性方面略有优势，而NNGT在提高噪声鲁棒性的前提下表现出准确性的下降。 结论：使用所提出的通用框架进行实时、内联参数估计解决了先进qMRI方法临床采用的关键实际障碍，并使其能够高效地集成到临床工作流程中。 显示英文摘要

摘要： Purpose: The clinical feasibility and translation of many advanced quantitative MRI (qMRI) techniques are inhibited by their restriction to 'research mode', due to resource-intensive, offline parameter estimation. This work aimed to achieve 'clinical mode' qMRI, by real-time, inline parameter estimation with a trained neural network (NN) fully integrated into a vendor's image reconstruction environment, therefore facilitating and encouraging clinical adoption of advanced qMRI techniques. Methods: The Siemens Image Calculation Environment (ICE) pipeline was customised to deploy trained NNs for advanced diffusion MRI parameter estimation with Open Neural Network Exchange (ONNX) Runtime. Two fully-connected NNs were trained offline with data synthesised with the neurite orientation dispersion and density imaging (NODDI) model, using either conventionally estimated (NNMLE) or ground truth (NNGT) parameters as training labels. The strategy was demonstrated online with an in vivo acquisition and evaluated offline with synthetic test data. Results: NNs were successfully integrated and deployed natively in ICE, performing inline, whole-brain, in vivo NODDI parameter estimation in <10 seconds. DICOM parametric maps were exported from the scanner for further analysis, generally finding that NNMLE estimates were more consistent than NNGT with conventional estimates. Offline evaluation confirms that NNMLE has comparable accuracy and slightly better noise robustness than conventional fitting, whereas NNGT exhibits compromised accuracy at the benefit of higher noise robustness. Conclusion: Real-time, inline parameter estimation with the proposed generalisable framework resolves a key practical barrier to clinical uptake of advanced qMRI methods and enables their efficient integration into clinical workflows.