Efficiency of low‐contrast detail detectability in fluoroscopic imaging

Abstract

The detectability of a static low-contrast detail in the dynamic fluoroscopic image of a homogeneous phantom was assessed by physical measurement of the signal-to-noise ratio (SNR) and by psychophysical measurement of the human observer detectability index d'. The two-alternative forced-choice method was used for human observer tests. The image data consisted of digitally recorded fluoroscopic image sequences which were displayed in a continuous loop of varying length (1-50 frames) at a rate of 25 frames/s. Human detection performance was seen to improve with the SNR in all cases studied: when the signal was made stronger, the image noise lower, or when the SNR in the image sequence was made higher by increasing the length of the image sequence. The results imply that the statistical efficiency of humans decreases slowly when the number of frames in the displayed loop is increased. This decrease of efficiency with loop length was not seen in all test series, however, and it is possible that the phenomenon is partly related to the high d' values found at the greatest loop lengths studied. When the display contrast was high, the statistical efficiency of the human observer was 30%-40% for both static and dynamic images. The efficiency was somewhat lower, 15%-25%, for images that were displayed with a display contrast gain setting more typical of fluoroscopy. The accumulation rate of SNR2 is a suitable quantity for the measurement of fluoroscopic image quality as related to a given static signal detection task. In contrast to this, visibility measurement by determination of the threshold contrast was seen to be unacceptably imprecise if the test is based on only one observer's opinion, as is often the case in practical quality assurance testing. The precision of the threshold contrast measurement could, however, be improved by using several observers and test objects with a smaller step between details than is usual.

Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms

“…The calculated efficiency (0.44) is in the range obtained by other authors (h 0.5) when applying the same model observer to other types of images. 14,18,29 The agreement between the model and human observer was excellent at the dose range considered in this work (10-250 mA) for both reconstruction algorithms after applying the h factor, as shown in Figure 6.…”

“…28,29 Only the human PC values below this threshold were used to determine the efficiency of the NPWE model observer. In Figure 5, the d9 values for the average human observer are plotted as a function of NPWE models (both squared).…”

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.…”

“…Only a few reports describe experiments using real images; however, these investigations did not implement channelized Hotelling observer (CHO) models as described below. 18,20,26 For hardware assessment, the Bayesian ideal observer or the ideal linear observer is desirable. 27 When considering object signal-to-noise ratio (SNR) as the model's figure of merit (FOM), the Hotelling model is the optimal linear observer.…”

Assessing the Usefulness of the Quasi-Ideal Observer for Quality Control in Fluoroscopy

“…The model observer used in this study has previously been described in detail (8)(9)(10) . In short, The DCsHFs-observer conditional decision variables (eq.1) were needed in (eq.2)…”