4.4 Review

Advances in micro-CT imaging of small animals

Journal

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2021.07.005

Keywords

Micro-CT; Spectral CT; Photon counting detector; Preclinical; Contrast agents; Nanoparticles; Theranostics; Phase contrast; Deep learning

Funding

  1. NIH National Cancer Institute [R01 CA196667, U24 CA220245]
  2. NIH National Institute of Aging [1RF1AG070149-01]

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This study reviews recent advancements in micro-CT technology for preclinical research, including new hardware features and various imaging applications such as dual-energy micro-CT and X-ray phase contrast imaging. Additionally, it discusses the integration of deep learning with micro-CT data for enhanced analysis and understanding.
Purpose: Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research. Methods: Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-theart hardware and ongoing challenges and promising research directions in the field. Results: Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy microCT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to microCT data, and we outline several challenges specific to micro-CT. Conclusions: All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

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