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Visual Computing for Industry, Biomedicine, and Art
Fig. 4 | Visual Computing for Industry, Biomedicine, and Art

Fig. 4

From: Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning

Fig. 4

Workflow of the SMEIR-Unet algorithm. Instead of feeding the DVFs into biomechanical modeling, SMEIR-Unet applies a U-net-based convolutional neural network to directly correct the output 2D-3D DVFs to boost their intra-lung accuracy. The corrected DVFs are output as the final inter-phase DVFs, and are applied in a final motion-compensated reconstruction step to generate the mCBCT at the reference phase, as well as the other 4D-CBCT phase images via the inverse DVFs. SMEIR-Unet: Simultaneous motion estimation and image reconstruction with U-net based DVF fine-tuning; DVF: Deformation-vector-field; 4D-CBCT: 4-Dimensional cone-beam computed tomography; mCBCT: Motion-compensated CBCT

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