Low contrast detection and super-resolution in CT images: evaluation of a novel approach based on Centroidal Voronoi Tessellation

Abstract

In this work, image analysis techniques used in astrophysics to detect low-contrast signals have been adapted in the processing of Computed Tomography (CT) images, combining Centroidal Voronoi Tessellation (CVT) and machine learning techniques. Several CT acquisitions were performed using a phantom containing cylindrical inserts of different diameters producing objects with different contrasts respect to background. The images of the phantom, tilted by a known angle with respect to the tomograph axis (to mimic the casual orientation of a clinical lesion), were acquired at various radiation doses (CTDIvol) and at different slice's thicknesses. The success in detecting the signal in the single image (slice) was always greater than 60%. The axis of each insert has always been correctly identified. A super-resolution 2D image was then generated by projecting the individual slices of the scan along this axis, thus increasing the CNR of the object scanned as a whole. CVT holds great promise for future use in medical imaging, for the identification of low-contrast lesions in homogeneous organs, such as the liver.