Sammendrag
Many adaptive algorithms claim to provide higher contrast than delay-and-sum (DAS). These claims are often backed by estimations of the contrast-to-noise ratio (CNR). Intuitively , we assume that higher CNR leads to higher probability of lesion detection, and this is indeed the case for DAS. However, non-linear processing can arbitrarily alter CNR, and yet yield no improvement in the detection probability. We propose a new image quality index, the generalized contrast-to-noise ratio (GCNR), based on the overlap area of the probability density function inside and outside the target area. GCNR can be used with non-linear beamforming algorithms, remaining unaltered if the dynamic range is changed. We demonstrate that GCNR is proportional to the maximum success rate that can be expected from the algorithm. Using Field II, we compare the performance of CNR and GCNR in 6 imaging algorithms. While CNR varies significantly between the 6 algorithms, we do not observe notable variations in GCNR (
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