Keywords: Computed tomography, sparse view CT, interventional CBCT, deep learning
TL;DR: Adds a segmentation loss to Dual Branch Prior-Net to improve the reconstruction
Abstract: This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment interventional instruments and thus guides the reconstruction task. The prior scans are misaligned by up to +-5deg in-plane during training. Experiments show that the proposed model, Dual Branch Prior-SegNet, significantly outperforms any other evaluated model by >2.8dB PSNR. It also stays robust wrt. rotations of up to +-5.5deg.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Image Acquisition and Reconstruction
Secondary Subject Area: Segmentation
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