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

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 learn- ing techniques. Several CT acquisitions were performed using a phantom containing cylindrical inserts of different diameters producing objects with different contrasts with respect to the 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 (CT DIvol) 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