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The invasive measurement of the AIF for the full quantification of dynamic PET data limits its widespread use in clinical research studies. Current methods which estimate the AIF from imaging data are prone to large errors, even when based on NNs. This work aims to estimate the AIF from dynamic PET images using physically informed deep neural networks. To this end, we employ 3D convolutions where we exploit the different channels to encode time-dependent information, and exploit depthwise separable convolutional layers to significantly reduce parameter count. We find that the incorporation of prior knowledge in the form of differentiable equations allows accurate estimation of the AIF. This allows kinetic modeling which leads to good estimates of the distribution volume. This work can pave the way for removing the large invasivity constraint that currently limits quantitative PET applications.
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Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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