|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SENSITIVITY TO POINT-SPREAD FUNCTION PARAMETERS IN MEDICAL ULTRASOUND IMAGE DECONVOLUTION
Ho-Chul Shin, Richard Prager, James Ng, Henry Gomersall, Nick Kingsbury, Graham Treece, and Andrew Gee
The resolution of ultrasound images can be improved by deconvolving the images with an estimate of the point-spread function. However, it is difficult to obtain an accurate estimate of the point-spread function in-vivo because of the unknown properties of the soft tissue through which the signal propagates. The purpose of this paper is to explore the sensitivity of a state-of-the-art deconvolution algorithm to uncertainty in the point-spread function. We present simulated and in-vitro sensitivity analyses of two-dimensional deconvolution while varying six parameters on which the point-spread function depends. The results are analysed both quantitatively and in terms of the perceived image quality. Our findings indicate that effective deconvolution can be performed without stringent tolerances on the accuracy of the assumed point-spread function. These findings are confirmed in a further experiment involving the deconvolution of an in-vivo ultrasound image.
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