PurposeTo propose the simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.MethodsThe SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC‐MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data.ResultsThe proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods.ConclusionThese results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground‐truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning.

中文翻译：

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SPINNED：基于模拟的物理信息神经网络，用于动态磁化率对比 MRI 灌注数据的反卷积

目的提出基于模拟的物理信息神经网络，用于动态磁化率对比 (DSC) MRI (SPINNED) 的反卷积，作为与现有方法相比更稳健和更准确的反卷积的替代方法。方法 SPINNED 方法是通过生成合成组织残差函数和通过数学模拟计算动脉输入函数，并使用它们创建合成 DSC MRI 时间序列。使用这些模拟数据训练 SPINNED 模型，以了解 DSC-MRI 时间序列和动脉输入函数之间的潜在物理关系（反卷积）。通过使用模拟数据和真实患者数据，将所提出的 SPINNED 方法与 DSC MRI 数据分析中两种常见的反卷积方法（循环奇异值分解和 Volterra 奇异值分解）进行比较，评估了所提出的 SPINNED 方法的准确性和鲁棒性。在所有 SNR 水平上都比传统方法更准确，并且在模拟和真实患者数据中都表现出更好的抗噪声鲁棒性。 SPINNED 方法还显示出比传统方法快得多的处理速度。结论这些结果支持所提出的 SPINNED 方法可以很好地替代现有方法来解决 DSC MRI 中的反卷积问题。所提出的方法不需要任何单独的地面实况测量来进行训练，并且提供了快速处理时间和覆盖不同临床场景的额外好处。因此，与之前的方法（包括基于监督学习的方法）相比，它将有助于临床应用中更可靠、更准确、更快速的诊断。