Spatial and frequency-based super-resolution of ultrasound images

Modern ultrasound systems can output video images containing more spatial and temporal information than still images. Super-resolution techniques can exploit additional information but face two challenges: image registration and complex motion. In addition, information from multiple available frequencies is unexploited. Herein, we utilised these information sources to create better ultrasound images and videos, extending existing technologies for image capture. Spatial and frequency-based super-resolution processing using multiple motion estimation and frequency combination was applied to ultrasound videos of deforming models. Processed images are larger, have greater clarity and detail, and less variability in intensity between frames. Significantly, strain measurements are more accurate and precise than those from raw videos, and have a higher contrast ratio between 'tumour' and 'surrounding tissue' in a phantom model. We attribute improvements to reduced noise and increased resolution in processed images. Our methods can significantly improve quantitative and qualitative assessments of ultrasound images when compared assessments of standard images.