用神经隐式表示提升动态PET图像的个性化代谢参数精度。
Physiological neural representation for personalised tracer kinetic parameter estimation from dynamic PET
- 用隐式神经表示建模生理信号，实现高分辨率参数成像。
- 相比现有方法，空间分辨率更高，误差更低，尤其在肿瘤区表现更好。
- 适合医学影像分析、肿瘤精准诊断与预后评估研究者使用。
动态正电子发射断层扫描（PET）结合[18F]FDG可无创量化葡萄糖代谢，通常采用双组织隔室模型（TCKM）进行动力学分析。然而，传统方法在体素级参数估计中计算量大且受空间分辨率限制。深度神经网络（DNNs）虽可替代，但需大量训练数据和算力。为此，我们提出基于隐式神经表示（INRs）的生理神经表征，用于个性化动力学参数估计。INRs通过学习连续函数，实现高效高分辨率参数成像，且对数据需求较低。方法还融合3D CT基础模型的解剖先验信息，提升建模鲁棒性与精度。我们在[18F]FDG动态PET/CT数据集上评估该方法，对比先进DNN模型。结果表明，本方法具有更优的空间分辨率、更低的均方误差，且在肿瘤及高血管化区域具备更强解剖一致性。研究揭示了INRs在个性化、数据高效示踪剂动力学建模中的潜力，适用于肿瘤表征、分割与预后评估。
原文摘要 · Abstract (English)
Dynamic positron emission tomography (PET) with [$^{18}$F]FDG enables non-invasive quantification of glucose metabolism through kinetic analysis, often modelled by the two-tissue compartment model (TCKM). However, voxel-wise kinetic parameter estimation using conventional methods is computationally intensive and limited by spatial resolution. Deep neural networks (DNNs) offer an alternative but require large training datasets and significant computational resources. To address these limitations, we propose a physiological neural representation based on implicit neural representations (INRs) for personalized kinetic parameter estimation. INRs, which learn continuous functions, allow for efficient, high-resolution parametric imaging with reduced data requirements. Our method also integrates anatomical priors from a 3D CT foundation model to enhance robustness and precision in kinetic modelling. We evaluate our approach on an [$^{18}$F]FDG dynamic PET/CT dataset and compare it to state-of-the-art DNNs. Results demonstrate superior spatial resolution, lower mean-squared error, and improved anatomical consistency, particularly in tumour and highly vascularized regions. Our findings highlight the potential of INRs for personalized, data-efficient tracer kinetic modelling, enabling applications in tumour characterization, segmentation, and prognostic assessment.
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