Virtual monoenergetic micro-CT imaging in mice with artificial intelligence

Abstract

Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudinal assessment of treatment outcomes. However, imaging artifacts such as beam hardening will occur due to the low energetic nature of the X-ray imaging beam (i.e., 60 kVp). Beam hardening artifacts are especially difficult to resolve in a ‘pancake’ imaging geometry with stationary source and detector, where the animal is rotated around its sagittal axis, and the X-ray imaging beam crosses a wide range of thicknesses. In this study, a seven-layer U-Net based network architecture (vMonoCT) is adopted to predict virtual monoenergetic X-ray projections from polyenergetic X-ray projections. A Monte Carlo simulation model is developed to compose a training dataset of 1890 projection pairs. Here, a series of digital anthropomorphic mouse phantoms was derived from the reference DigiMouse phantom as simulation geometry. vMonoCT was trained on 1512 projection pairs (= 80%) and tested on 378 projection pairs (= 20%). The percentage error calculated for the test dataset was 1.7 ± 0.4%. Additionally, the vMonoCT model was evaluated on a retrospective projection dataset of five mice and one frozen cadaver. It was found that beam hardening artifacts were minimized after image reconstruction of the vMonoCT-corrected projections, and that anatomically incorrect gradient errors were corrected in the cranium up to 15%. Our results disclose the potential of Artificial Intelligence to enhance the µCBCT image quality in biomedical applications. vMonoCT is expected to contribute to the reproducibility of quantitative preclinical applications such as precision irradiations in X-ray cabinets, and to the evaluation of longitudinal imaging data in extensive preclinical studies.

Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

“…Despite the abundant research work on scatter artifact corrections, studies tackling beam hardening are scarce. One such study involved training a U-Net-based architecture to predict monoenergetic X-ray projections from polyenergetic X-ray projections using supervised learning on Monte Carlo simulation-based ground truth in the projection domain [91]. Simulated 4D CBCT scan at three distinct motion phases, without significant motion artifacts Sparse-view artifacts at various sub-sampling rates (from left to right: 1/6, 1/18 and 1/48) Limited angle artifacts [12] Scatter artifacts [87] Metal artifacts [88] Motion artifacts in simulated (left) and real (middle and right) CBCT scans [24] FIGURE 3: Examples of different kinds of artifacts appearing in CBCT scans.…”