Higher order spectra based deconvolution of ultrasound images

摘要

We address the problem of improving the spatial resolution ofnulrasound images through blind deconvolution. The ultrasound imagenformation process in the RF domain can be expressed as a spatio-temporalnconvolution between the tissue response and the ultrasonic systemnresponse, plus additive noise. Convolutional components of thendispersive attenuation and aberrations introduced by propagating throughnthe object being imaged are also incorporated in the ultrasonic systemnresponse. Our goal is to identify and remove the convolutionalndistortion in order to reconstruct the tissue response, thus enhancingnthe diagnostic quality of the ultrasonic image. Under the assumption ofnan independent, identically distributed, zero-mean, non-Gaussian tissuenresponse, we were able to estimate distortion kernels using bicepstrumnoperations on RF data. Separate 1D distortion kernels were estimatedncorresponding to axial and lateral image lines and used in thendeconvolution process. The estimated axial kernels showed similaritiesnto the experimentally measured pulse-echo wavelet of the imaging system.nDeconvolution results from B-scan images obtained with clinical imagingnequipment showed a 2.5-5.2 times gain in lateral resolution, where thendefinition of the resolution has been based on the width of thenautocovariance function of the image. The gain in axial resolution wasnfound to be between 1.5 and 1.9