用鲁棒核表示法提升低剂量PET图像重建的泛化能力
Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction
- 通过鲁棒核表示显式建模深层潜在特征
- 在10到1000倍剂量降低下性能显著优于传统方法
- 适合需要应对未知剂量条件的医学影像研究
低剂量正电子发射断层扫描（PET）图像重建方法有望显著提升PET成像的实用性。深度学习为将先验信息融入图像重建问题提供了有力手段，可在信号受损的情况下生成定量准确的图像。然而，现有的基于深度学习的低剂量PET方法通常训练条件不佳，在训练分布之外的特征上表现不可靠。本文提出一种方法，通过鲁棒核表示显式建模深层潜在空间特征，实现了对未见剂量降低因子的稳健性能。对深层潜在特征的信息量施加额外约束，可调节模型在分布内精度与泛化能力之间的平衡。在剂量降低因子从×10到×1000的分布外测试中，无论是否配对MR数据，均显著优于使用相同数据训练的传统深度学习方法。
原文摘要 · Abstract (English)
Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction problem to produce quantitatively accurate images from compromised signal. Deep learning-based methods for low-dose PET are generally poorly conditioned and perform unreliably on images with features not present in the training distribution. We present a method which explicitly models deep latent space features using a robust kernel representation, providing robust performance on previously unseen dose reduction factors. Additional constraints on the information content of deep latent features allow for tuning in-distribution accuracy and generalisability. Tests with out-of-distribution dose reduction factors ranging from $\times 10$ to $\times 1000$ and with both paired and unpaired MR, demonstrate significantly improved performance relative to conventional deep-learning methods trained using the same data. Code:https://github.com/cameronPain
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