Perception of temporally filtered X-ray fluoroscopy images

Abstract

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.

Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems

“…6,10,11 Additionally, application of linear observer models to fluoroscopic and angiographic imaging systems has been investigated. [13][14][15][16][17][18][19][20][21][22][23][24][25][26] However, the vast majority of the reports feature investigations on simulated data. [13][14][15][16][17]19,[21][22][23][24][25] Although simulated data have significant merits for system design, data acquired from real imaging systems are required to assess real-world system performance.…”

Overcoming x-ray tube small focal spot output limitations for high resolution region of interest imaging

“…The equation used for a basic recursive temporal filtering is given by [14,15] $$\mathrm{Y}(\mathrm{t})=\frac{1}{\mathrm{normalk}}\times \mathrm{X}(\mathrm{t})+\left(1-\frac{1}{\mathrm{k}}\right)\mathrm{Y}(\mathrm{t}-1)$$ where Y(t) is the output of the filter, X(t) is the current input signal at time or frame number t, Y(t−1) is the previous frame’s output signal, and k is the filter weight (lag or memory). The output of the filter is the weighted sum of its current input and previous outputs.…”

Stent enhancement in digital x-ray fluoroscopy using an adaptive feature enhancement filter

“…There are also extraordinary activities in improving fluoroscopic image quality by applying digital image processing techniques. Some filters were studied including temporal [9], spatial [10], spatial-temporal [11], unsharp-masking [12][13] etc. Another important method is to use feature enhancement filters to selectively increase the contrast of object without dramatically boosting the noise.…”