Towards Patient Specific Reconstruction Using Perception-Aware Cnn And Planning Ct As Prior

The C-arm Cone-Beam Computed Tomography (CBCT) increasingly plays a major role in interventions and radiotherapy. However, the slow data acquisition and high dose hinder its predominance in the clinical routine. To overcome the high-dose issue, various protocols such as sparse-view have been proposed, where a subset of projections is acquired over increased angular steps. However, applying the standard reconstruction algorithms to datasets obtained from such protocols results in volumes with severe streaking artifacts. Further, the presence of surgical instruments worsens the quality and make the reconstructions clinically useless. High-quality pre-operative CT scans are usually acquired for diagnosis and intervention planning, which contain the required high-resolution details of the body part. In this work, we propose a deep learning-based method that incorporates the planning CT along with the sparse-sampled interventional CBCT of the same subject to produce high-quality reconstructions containing the surgical instrument. We also propose a perception-aware loss for the task, which facilitates the model to capture the surgical instrument precisely, compared to the pixelwise mean squared error (MSE). The model using the planning CT and trained with pixelwise MSE loss improves the soft-tissue contrast and the reconstruction quality (mean PSNR) from 27.46dB to 32.89dB. The perception-aware loss further improves the reconstruction quality statistically significantly to 36.14dB.