Application of big data analytics for automated estimation of CT image quality

With the increasing applications of Big Data analytics in medical image processing systems, there has been a growing need for quantitative medical image quality assessment techniques. Specifically for computed tomography (CT) images, quantitative image assessment can allow for benchmarking image processing methods and optimization of image acquisition parameters. In this work, large volumes of CT images from phantoms and patients are analyzed using 3 data models that vary in their implementation time complexities. The goal here is to identify the optimal method that scales across data set variabilities for predictive modeling of CT image quality (CTIQ). The first two models rely on spatial segmentation of regions-of-interest (ROIs) and estimate CTIQs in terms of segmented pixel variabilities. The third, convolutional neural network (CNN) model relies on error back-propagation from the training set of images to learn the regions indicative of CTIQ. We observe that for 70/30 data split, the average multi-class classification accuracies for CTIQ prediction using the 3 data models range from 73.6-100% and 50-100% for the phantom and patient CT images, respectively. Using variance of pixels within the segmented ROIs as a CTIQ classification parameter, the spatial segmentation data models are found to be more generalizable that the CNN model. However, the CNN model is found to be more suitable for CT image texture classification in the absence of structural variabilities. Our analysis demonstrates that spatial ROI segmentation data models are consistent CTIQ estimators while the CNN models are consistent identifiers of structural similarities for CT image data sets.