CitrusGAN: sparse-view X-ray CT reconstruction for citrus based on generative adversarial networks.

ABSTRACT: 3D phenotyping of the external and internal structures is important to breed new fruit species. As manual phenotyping is error-prone and time-consuming, developing high-throughput solutions with enhanced precision and low costs is necessary. This study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images. The input X-rays are arranged in orthogonal pairs to provide additional information, and customized loss functions enable more effective learning of the mapping from 2D X-ray features to 3D CT volumes. Experimental results show that 6 views can generate high-quality citrus CT volumes, with a structural similarity index of 92.1 ​% and a peak signal-to-noise ratio of 26.374 ​dB compared with the real CT models. Mo

SUBMITTER: Xiang H 

PROVIDER: S-EPMC12709887 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature