For noisy X-ray fluoroscopy image sequences we quantitatively evaluated image quality after digital temporal filtering to reduce noise. Using an experimental paradigm called a reference/test adaptive forced-choice method we compared detectability of stationary low-contrast disks in filtered and unfiltered, computer-generated image sequences. In the first experiment, a low-pass first-order recursive filter used in X-ray fluoroscopy was found to be much less effective at enhancing detectability than predicted from the reduction of display noise variance, a common measurement of filter effectiveness. Detectability was reasonably predicted by a nonprewhitening human-observer model (NPW-HVS) that included an independently determined human temporal-contrast-sensitivity function. In another experiment, designed to test models over a range of temporal frequencies, we used paired high-pass and low-pass temporal filters that both reduced noise variance by 25%. The high-pass filter was artificially applied to the noise only and greatly improved detectability, while the low-pass filter had little effect. The human-observer model quantitatively described the measurements, but classical prewhitening and nonprewhitening signal detectors did not. As compared to the nonprewhitening, spatio-temporal matched filter, human-observer efficiency was low and variable at 2.1%, 2.9%, and 0.06% for 60 frames of unfiltered low-pass and high-pass noise, respectively. As compared to this detector, humans were not very effective at combining information across frames. On the other hand, signal to noise ratios (SNR's) from the human-observer model were comparable to human performance, and efficiencies were reasonably constant at 40%, 52%, and 32%, respectively. We conclude that it is imperative to include human-observer models and experiments in the analysis of noise-reduction filtering of noisy image sequences, such as X-ray fluoroscopy.

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Digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences.

The effect of spatial noise-reduction filtering on human observer detection of stationary cylinders mimicking arteries, catheters, and guide wires in x-ray fluoroscopy was investigated in both single image frames and image sequences. Ideal edge-preserving spatial filtering was simulated by filtering of the noise before addition of the target cylinder. This allowed us to separate the effect of edge blurring from those of noise reduction and spatial noise correlation. We used three different center-weighted averagers that reduced pixel noise variance by factors of 0.75, 0.50, and 0.25. As compared with no filtering, the effect of filtering on detection in single images was statistically insignificant. This indicated an adverse effect of spatial noise correlation on detection that countered the effect of noise reduction. By comparison, spatial filtering significantly improved detection in image sequences and yielded potential x-ray dose savings of 26-34%. Comparison of results with two observer models suggested that human observers have an improved detection efficiency in spatially filtered image sequences as compared with white-noise sequences. Pixel noise reduction, a measure commonly used to assess filter performance, overestimated the effect of filtering on detection and was not a good indicator of image quality. We conclude that edge-preserving spatial filtering is more effective in sequences than in single images and that such filtering can be used to improve image quality in noisy image sequences such as x-ray fluoroscopy.

Spatial unsharp-mask processing and its variants are commonly used in x-ray radiography to enhance image contrast. We investigated the effect of three unsharp-masking filter kernels of different sizes on the detection of an advanced guidewire tip in simulated x-ray fluoroscopy image sequences. To isolate the effect of visual temporal processing, we repeated the experiments on single images. Filter gains were selected so that all three kernels increased the contrast of a 0.018-in. (0.457-mm) guidewire by a factor of 2 but had different effects on image noise and signal profiles. There was no statistically significant effect of unsharp masking on human-observer performance in single images. However, all three kernels significantly improved average performance in image sequences, and the guidewire contrast required for detection was reduced by 32%-40%. A prewhitening channelized observer model predicted the disparity between sequences and single images and fitted measurements at different kernel sizes well. A nonprewhitening observer model did not. We conclude that unsharp masking is a simple and effective method of improving guidewire visualization in fluoroscopically guided interventional procedures and that quantitative image quality studies are essential for evaluation of image-processing techniques in sequences such as x-ray fluoroscopy.