Abstract:
DPC-CT is useful for soft-tissue and low-atomic-number sample analysis. Due to implementation constraints, DPC-CT often has incomplete projections. Incomplete data challenges conventional reconstruction algorithms.
They involve time-consuming, noisy parameter selection operations. We present DPC-CT, a deep learning reconstruction framework for incomplete data. The deep learning neural network and DPC-CT reconstruction algorithm are tightly coupled in DPC projection sinograms. A complete phase-contrast projection sinogram was estimated.
This framework can reconstruct DPC-CT images from incomplete projection sinograms after training. Synthetic and experimental data sets validate and demonstrate this framework using sparse-view, limited-view, and missing-view DPC-CT.
Our framework provides the best imaging quality at a faster speed and fewer parameters than other methods. This study encourages DPC-CT researchers to use cutting-edge deep learning theory.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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