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Deep learning-aided CBCT image reconstruction of interventional material from four x-ray projections
In this work we develop a novel deep learning-based approach to reconstruct interventional tools from only four x-ray projections. We train and test this deep tool reconstruction (DTR) network on simulated data. Only small deviations from the ground truth (GT) reconstruction of the tools were observed, both quantitatively and qualitatively, showing that deep learning-based four-dimensional interventional guidance has the potential to overcome the drawbacks of conventional interventional guidance in the future.
On the impact of input feature selection in deep scatter estimation for positron emission tomography
Reanimating patients: cardio-respiratory CT and MR motion phantoms based on clinical CT patient data
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