Anjidani, Farid2023-05-182023-05-1820232023-05-18http://hdl.handle.net/1828/15120Plane-wave ultrasound imaging allows for very high frame rates. During image reconstruction, conventional delay-and-sum beamforming can be replaced by the quicker Fourier-domain remapping method. Typically, after Fourier-domain reconstruction, postbeamforming interpolation is needed to increase the image grid resolution in the lateral dimension. To achieve this, we propose to use a fast lightweight superresolution convolutional neural network (CNN) operating on the Fourier-beamformed envelope data. Specifically, we train different configurations of well-known Efficient Sub-Pixel Convolutional Neural Network (ESPCN) to perform both 1D and 2D upscaling. First, we pretrain a network using the diverse (non-ultrasound) dataset DIV2K. Then, we apply transfer learning on a small augmented dataset of public-domain experimental ultrasound images. Our results demonstrate that our approach is capable of producing enhanced ultrasound images having higher quality compared to non-CNN interpolation options and conventional delay-and-sum beamforming.enAvailable to the World Wide WebBicubic InterpolationCoherent Plane-Wave CompoundingConvolutional Neural NetworkDelay-and-Sum (Beamforming)Fast Fourier TransformFourier InterpolationFull Width at Half MaximumMean Squared Logarithmic ErrorPlane Wave ImagingTemme-Mueller (Migration)UltraSoundSingle-Image Super-ResolutionbeamformingDASsuperresolutionThesis