In this work we consider a well-known empirical beam-hardening correction algorithm applied to metal artifact reduction in cone beam micro-computed tomography (CT). In its basic form, this algorithm consists of a few simple steps: segmentation of metal components out of uncorrected image data, forward projection to create metal only projection data, and the creation of several correction basis images. The set of basis images can then be combined to create a corrected image with mitigated metal artifacts. The combination weights can be determined either manually or automatically by solving an optimization equation, be performed globally or locally (allowing weights to vary spatially). The latter approach may be more attractive as it may account for larger local variances in scatter artifacts, however, not practical for manual optimization, requiring an automated approach. We apply both global and spatially variant version of the algorithm to datasets from a cone-beam micro-CT (using a 3D X-ray microscope) and study the performance.
A novel automated workflow for the recovery of image resolution using deep convolutional neural networks (CNNs) trained using spatially registered multiscale data is presented. Spatial priors, coupled with high order voxel-based image registration, are used to correct for uncertainties in image magnification and position. A network is then trained to remove the effects of point spread from the low-resolution data, improving resolution while reducing image noise and artefact levels. While benchmarking on real materials, including biological, materials science and electronics samples, we find that resolution recovery improves quantitative and qualitative measurements, even if certain image details cannot be easily identified from the original low-resolution data.
Metal artifacts are one of the most common reasons for reduced image quality and usability in polychromatic cone-beam CT. In this work, we revisit empirical beam hardening correction algorithm and propose a few practical optimizations to simplify its application. First, fuzzy C-means segmentation method is used to perform an automatic segmentation of the metal component. Second, a minimum variance optimization technique provides a suitable combination of correction basis images. Finally, a sub-volume (spatially varying) optimization method is used to account for a varying contribution of metal artifacts through the image. We apply the modified algorithm to datasets from cone-beam CT and evaluate its performance.
A novel automated workflow for the recovery of image resolution using deep convolutional neural networks (CNNs) trained using spatially registered multiscale data is presented. Spatial priors, coupled with high order voxel-based image registration, are used to correct for uncertainties in image magnification and position. A network is then trained to remove the effects of point spread from the low-resolution data, improving resolution while reducing image noise & artefact levels. While benchmarking on real materials, including biological, materials science and electronics samples, we find that resolution recovery improves quantitative and qualitative measurements, even if certain image details cannot be easily identified from the original low-resolution data.
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