  Current mainstream of CT reconstruction methods based on deep learning
usually needs to fix the scanning geometry and dose level, which will
significantly aggravate the training cost and need more training data for
clinical application. In this paper, we propose a parameter-dependent framework
(PDF) which trains data with multiple scanning geometries and dose levels
simultaneously. In the proposed PDF, the geometry and dose level are
parameterized and fed into two multi-layer perceptrons (MLPs). The MLPs are
leveraged to modulate the feature maps of CT reconstruction network, which
condition the network outputs on different scanning geometries and dose levels.
The experiments show that our proposed method can obtain competing performance
similar to the original network trained with specific geometry and dose level,
which can efficiently save the extra training cost for multiple scanning
geometries and dose levels.
