Quantitative Study Of Deconvolution And Display Mappings For Long-Tailed Point-Spread Functions

An important goal in medical imaging is to increase the accuracy of visual detection of small abnormal regions. The presence of scatter in the image degrades spatial resolution by introducing long tails to the point-spread function. We show in this paper that linear deconvolution can be used to improve the performance of the human observer in the two-hypothesis detection task. Also, we investigate the effect that linear grey-scale mappings have on the human observer performance. We demonstrate that they help the human observer in the detection task and can be used sequentially with deconvolution to yield a better performance.