Dynamic positron emission tomography imaging (dPET) provides temporally
resolved images of a tracer enabling a quantitative measure of physiological
processes. Voxel-wise physiologically-based pharmacokinetic (PBPK) modeling of
the time activity curves (TAC) can provide relevant diagnostic information for
clinical workflow. Conventional fitting strategies for TACs are slow and ignore
the spatial relation between neighboring voxels. We train a spatio-temporal
UNet to estimate the kinetic parameters given TAC from F-18-fluorodeoxyglucose
(FDG) dPET. This work introduces a self-supervised loss formulation to enforce
the similarity between the measured TAC and those generated with the learned
kinetic parameters. Our method provides quantitatively comparable results at
organ-level to the significantly slower conventional approaches, while
generating pixel-wise parametric images which are consistent with expected
physiology. To the best of our knowledge, this is the first self-supervised
network that allows voxel-wise computation of kinetic parameters consistent
with a non-linear kinetic model. The code will become publicly available upon
acceptance.

本论文介绍了使用自监督方法构建环境下的动态正电子发射断层成像 (dPET) 成像技术，采用基于生理的药物动力学建模和空时域 UNet 来估计动力学参数的方法，以及像 FDG 这样的放射性示踪剂的时间活动曲线来实现诊断性信息提取，产生与预期生理一致的像素级参数图像。

动态 PET 中基于生理学的药物动力学建模的自监督学习

Self-Supervised Learning for Physiologically-Based Pharmacokinetic  Modeling in Dynamic PET

The self-supervised loss formulation for jointly training depth and egomotion
neural networks with monocular images is well studied and has demonstrated
state-of-the-art accuracy. One of the main limitations of this approach,
however, is that the depth and egomotion estimates are only determined up to an
unknown scale. In this paper, we present a novel scale recovery loss that
enforces consistency between a known camera height and the estimated camera
height, generating metric (scaled) depth and egomotion predictions. We show
that our proposed method is competitive with other scale recovery techniques
that require more information. Further, we demonstrate that our method
facilitates network retraining within new environments, whereas other
scale-resolving approaches are incapable of doing so. Notably, our egomotion
network is able to produce more accurate estimates than a similar method which
recovers scale at test time only.

本文提出一种新的尺度恢复 loss 方法，通过强制实现已知摄像机高度和估计出的摄像机高度之间的一致性，以生成度量（缩放）深度和自运动预测。结果表明，我们提出的方法在网络重新训练方面具有其他尺度恢复技术无法实现的优势，并且我们的自运动网络能够比仅在测试时恢复尺度的类似方法产生更准确的估计结果。