Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction

Autor:	Ernst, Philipp, Chatterjee, Soumick, Rose, Georg, Nürnberger, Andreas
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Primal-Dual Network when using only 23 projections. The presented network is not optimized wrt. memory consumption or hyperparameters but merely serves as a proof of concept and is limited to low resolution projections and volumes. Comment: 3 pages, 1 figure, accepted short paper submission at MIDL 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.07866 Zobrazit plný text záznamu View this record from Arxiv