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In limited angle tomography, missing data in an insufficient angular scan will cause streak artifacts in the reconstructed images.Correspondingly, in the frequency domain representation of the imaged object, a double wedge-shaped region is missing.In this paper, we perform a regression in sinogram domain and an image fusion in frequency domain to restore the missing data. We first convert the sinogram restoration problem into a regression problem based on the Helgason-Ludwig consistency conditions. Due to its severe ill-posedness, regression only partially recovers the correct frequency components, especially lower frequency components, and will introduce erroneous ones, particularly higher frequencies. Bilateral filtering is utilized to retain the most prominent high frequency components and suppress erroneous ones. Afterwards, a fusion in the frequency domain utilizes the restored frequency components to fill the missing double wedge region. The proposed method is evaluated in a parallel-beam study on both numerical and clinical phantoms. The root-mean-square errors of the reconstructed images decrease from 302 to 78 HU for the noise-free Shepp-Logan phantom, from 355 to 175 HU for the noisy Shepp-Logan phantom, and from 187 to 56 HU for the clinical data. The results show that our method is promising in streak reduction and intensity offset compensation in both noise-free and noisy situations.

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the goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. one problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (Roi) are calibrated by further Rois such as air, adipose tissue, liver, etc. that are used as control regions (cR). our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by statusquo radiomics stability analysis techniques to only collect information from the cRs which is relevant given a respective task. these criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the cRs. our calibration enhanced the performance for centrilobular emphysema prediction in a copD study and prediction of patients' one-year-survival in an oncological study.