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Current mainstream of CT reconstruction methods based on deep learningusually needs to fix the scanning geometry and dose level, which willsignificantly aggravate the training cost and need more training data forclinical application. In this paper, we propose a parameter-dependent framework(PDF) which trains data with multiple scanning geometries and dose levelssimultaneously. In the proposed PDF, the geometry and dose level areparameterized and fed into two multi-layer perceptrons (MLPs). The MLPs areleveraged to modulate the feature maps of CT reconstruction network, whichcondition the network outputs on different scanning geometries and dose levels.The experiments show that our proposed method can obtain competing performancesimilar to the original network trained with specific geometry and dose level,which can efficiently save the extra training cost for multiple scanninggeometries and dose levels.

CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels