AIDM-CT: software for cone-beam X-ray tomography and deep-learning-based analysis

Abstract

ABSTRACT Non-destructive testing (NDT) with high-resolution cone-beam X-ray computed tomography (XCT) plays a crucial role in revealing the 3D distribution and morphology of defects within an object. It’s necessary to generalise an intelligent and customised XCT-based NDT protocol, providing solutions for reconstruction quality improvement and complex defect quantitative analysis for users. We analysed the key techniques of XCT and developed the systematic software for data acquisition, reconstruction, intelligent super-resolution and segmentation in our advanced imaging and data mining laboratory (AIDM-CT). The experimental results demonstrated the software efficiently achieves the control of XCT scanning, artefact correction algorithms related to cone-beam geometrics, beam hardening, ring and radial artefacts, deep-learning based slice super-resolution and feature segmentation, and quantitative analysis. The software is programmed by Python language to provide the friendly multi-function graphical user interface (GUI), in which the programming of reconstruction and corrections combined with Computer Unified Device Architecture (CUDA) acceleration to guarantee the high efficiency of XCT task. Particularly, the multi-functional super resolution module (MF-GAN) is proposed to optimise the quality of CT image and the improved U-Net segmentation module (CBAM U-Net) is also developed to fulfill the high-precision quantitative non-destructive testing of homogeneous and variform microdefects in our AIDM-CT software.