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.
Highlights
Micro Computed Tomography (CT) imaging is currently indispensable in preclinical small animal research
The Xstrahl SARRP system adopts a different so-called table-rotating ‘pancake’ g eometry[10] with stationary source and detector, where the prone-positioned animal is rotated around its sagittal axis, and the imaging beam traverses a wide range of thicknesses through the geometry being imaged[11]
Several effective Artificial Intelligence (AI) applications resolved the problem of X-ray scatter accurately, and they even accelerated the calculation of adaptive radiotherapy plans on clinical cone-beam CT (CBCT) images[22,23]
Summary
Micro Computed Tomography (CT) imaging is currently indispensable in preclinical small animal research. The Xstrahl SARRP system adopts a different so-called table-rotating ‘pancake’ g eometry[10] with stationary source and detector, where the prone-positioned animal is rotated around its sagittal axis, and the imaging beam traverses a wide range of thicknesses through the geometry being imaged[11]. Photon scatter leads to a contaminating particle fluence measured by the detector panel, but in preclinical low-energy X-ray imaging, it is primarily beam hardening that introduces a degraded image quality. The polynomial coefficients are determined from calibration scans of different-sized homogeneous cylindrical phantoms This correction algorithm assumes a conventional CBCT imaging setup wherein the source and the detector rotate simultaneously around the longitudinal axis of the object being imaged. The ‘pancake’ geometry-based systems are subject to beam hardening, because the imaging beam crosses a wide range of animal thicknesses
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