Ultrasparse View X-ray Computed Tomography for 4D Imaging

Yanjie Zheng, Kelsey B. Hatzell

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

X-ray computed tomography (CT) is a noninvasive, nondestructive approach to imaging materials, material systems, and engineered components in two and three dimensions. Acquisition of three-dimensional (3D) images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such 3D data acquisition strategies commonly involve suboptimal temporal sampling for in situ and operando studies (4D imaging). Herein, we introduce a sparse-view imaging approach, Tomo-NeRF, which is capable of reconstructing high-fidelity 3D images from <10 two-dimensional radiographic images. Experimental 2D and 3D X-ray images were used to test the reconstruction capability in two-view, four-view, and six-view scenarios. Tomo-NeRF is capable of reconstructing 3D images with a structural similarity of 0.9971-0.9975 and a voxel-wise accuracy of 81.83-89.59% from 2D experimentally obtained images. The reconstruction accuracy for the experimentally obtained images is less than the synthetic structures. Experimentally obtained images demonstrate a similarity of 0.9973-0.9984 and a voxel-wise accuracy of 84.31-95.77%.

Original languageEnglish (US)
Pages (from-to)35024-35033
Number of pages10
JournalACS Applied Materials and Interfaces
Volume15
Issue number29
DOIs
StatePublished - Jul 26 2023

All Science Journal Classification (ASJC) codes

  • General Materials Science

Keywords

  • X-ray computed tomography
  • neural radiance field
  • operando imaging
  • sparse reconstruction
  • tomographic reconstruction

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