Real-time Volumetric Imaging for Minimally-Invasive Thoracic Interventions: A Deformable Phantom Study

Objectives: This study aimed to evaluate the ability of VoxelMap, a deep learning framework for 3D respiratory motion estimation, to generate volumetric images in real-time from 2D projections acquired for two deformable lung phantoms on a clinical interventional C-arm angiography system. Materials and Methods: VoxelMap was evaluated using the PixelPrint4D and Casper deformable physical thoracic respiratory phantoms. Four-dimensional cone-beam CT (4D CBCT) images acquired on a clinical fixed-room C-arm angiography system were used for network training and volumetric ground truth. Prediction ac

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