In many applications based on kinetic evaluation analysis and model fitting, quantitative mapping retrieved on data series from modalites such as MRI is completed on a voxel-by-voxel basis, where motion and low signal to noise ratio (SNR) would considerably degenerate the reliability of estimations. The coherence of image series in space and time can be used as prior knowledge to mitigate this occurrence. In this study, spatial and temporal higher-order total variations (HOTVs) are applied on a data series of MRI signal (e.g. dynamic contrast-enhanced (DCE) MRI and intravoxel incoherent motion (IVIM) MRI) to exploit the coherence of signal in space and time to minimize the variabilities caused by motion as well as improving quality of images with low SNR while retaining the physical details of original data properly. Simultaneously applying spatial and temporal HOTVs on images is non-trivial in implementation since it is a non-smooth optimization problem with multiple regularizers. Therefore, we use the proximal gradient method as well as a primal-dual split proximal mechanism to address the problem properly. In addition to increase the reliability of quantitative parametric map estimations, this preprocessing procedure can be included into many existing map estimation algorithms and pipelines effortlessly. We demonstrate our method on the parametric maps estimation for DCE MRI and IVIM MRI.
Radiomics studies often analyze patient computed tomography (CT) images acquired from different CT scanners. This may result in differences in imaging parameters, e.g. different manufacturers, different acquisition protocols, etc. However, quantifiable differences in radiomics features can occur based on acquisition parameters. A controlled protocol may allow for minimization of these effects, thus allowing for larger patient cohorts from many different CT scanners. In order to test radiomics feature variability across different CT scanners a radiomics phantom was developed with six different cartridges encased in high density polystyrene. A harmonized protocol was developed to control for tube voltage, tube current, scan type, pitch, CTDIvol, convolution kernel, display field of view, and slice thickness across different manufacturers. The radiomics phantom was imaged on 18 scanners using the control protocol. A linear mixed effects model was created to assess the impact of inter-scanner variability with decomposition of feature variation between scanners and cartridge materials. The inter-scanner variability was compared to the residual variability (the unexplained variability) and to the inter-patient variability using two different patient cohorts. The patient cohorts consisted of 20 non-small cell lung cancer (NSCLC) and 30 head and neck squamous cell carcinoma (HNSCC) patients. The inter-scanner standard deviation was at least half of the residual standard deviation for 36 of 49 quantitative image features. The ratio of inter-scanner to patient coefficient of variation was above 0.2 for 22 and 28 of the 49 features for NSCLC and HNSCC patients, respectively. Inter-scanner variability was a significant factor compared to patient variation in this small study for many of the features. Further analysis with a larger cohort will allow more thorough analysis with additional variables in the model to truly isolate the interscanner difference.