In this article, we introduce a new ring artifacts reduction procedure that combines several ideas from existing methods into one complex and robust approach with a goal to overcome their individual weaknesses and limitations. The procedure differentiates two types of ring artifacts according to their cause and character in computed tomography (CT) data. Each type is then addressed separately in the sinogram domain. The novel iterative schemes based on relative total variations (RTV) were integrated to detect the artifacts. The correction process uses the image inpainting, and the intensity deviations smoothing method. The procedure was implemented in scope of lab-based X-ray nano CT with detection systems based on charge-coupled device (CCD) and scientific complementary metal–oxide–semiconductor (sCMOS) technologies. The procedure was then further tested and optimized on the simulated data and the real CT data of selected samples with different compositions. The performance of the procedure was quantitatively evaluated in terms of the artifacts’ detection accuracy, the comparison with existing methods, and the ability to preserve spatial resolution. The results show a high efficiency of ring removal and the preservation of the original sample’s structure.

X-ray computed tomography is a common tool for non-destructive testing and analysis. One major application of this imaging technique is 3D porosity identification and quantification, which involves image segmentation of the analysed dataset. This segmentation step, which is most commonly performed using a global thresholding algorithm, has a major impact on the results of the analysis. Therefore, a thorough description of the workflow and a general uncertainty estimation should be provided alongside the results of porosity analysis to ensure a certain level of confidence and reproducibility. A review of current literature in the field shows that a sufficient workflow description and an uncertainty estimation of the result are often missing. This work provides recommendations on how to report the processing steps for porosity evaluation in computed tomography data using global thresholding, and reviews the methods for the estimation of the general uncertainty in porosity measurements.

We report a reproducible preparation and characterization of highly homogeneous thermoplastic starch/pol(ε‑caprolactone) blends (TPS/PCL) with a minimal thermomechanical degradation and co-continuous morphology. These materials would be suitable for biomedical applications, specifically for the local release of antibiotics (ATB) from the TPS phase. The TPS/PCL blends were prepared in the whole concentration range. In agreement with theoretical predictions based on component viscosities, the co-continuous morphology was found for TPS/PCL blends with a composition of 70/30 wt.%. The minimal thermomechanical degradation of the blends was achieved by an optimization of the processing conditions and by keeping processing temperatures as low as possible, because higher temperatures might damage ATB in the final application. The blends’ homogeneity was verified by scanning electron microscopy. The co-continuous morphology was confirmed by submicron-computed tomography. The mechanical performance of the blends was characterized in both microscale (by an instrumented microindentation hardness testing; MHI) and macroscale (by dynamic thermomechanical analysis; DMTA). The elastic moduli of TPS increased ca four times in the TPS/PCL (70/30) blend. The correlations between elastic moduli measured by MHI and DMTA were very strong, which implied that, in the future studies, it would be possible to use just micromechanical testing that does not require large specimens.