Flattening of the contrast-detail curve for large lesions on liver CT images

Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm

Synthetic yet realistic images are valuable for many applications in visual sciences and medical imaging. Typically, investigators develop algorithms and adjust their parameters to generate images that are visually similar to real images. In this study, we used a genetic algorithm and an objective, statistical similarity measure to optimize a particular texture generation algorithm, the clustered lumpy backgrounds (CLB) technique, and synthesize images mimicking real mammograms textures. We combined this approach with psychophysical experiments involving the judgment of radiologists, who were asked to qualify the visual realism of the images. Both objective and psychophysical approaches show that the optimized versions are significantly more realistic than the previous CLB model. Anatomical structures are well reproduced, and arbitrary large databases of mammographic texture with visual and statistical realism can be generated. Potential applications include detection experiments, where large amounts of statistically traceable yet realistic images are needed.

Application of the noise power spectrum in modern diagnostic MDCT: part II. Noise power spectra and signal to noise

Balancing dose and image quality requires signal-to-noise (SNR) metrics which incorporate both the variance and the spatial frequency characteristics of noise. In this study, the non-prewhitening matched filter SNR metric is calculated for 2 mm slices of a 1 cm diameter sphere under three different conditions: (1) constant pixel standard deviation, (2) constant dose and (3) constant reconstruction filter. For the constant pixel standard deviation condition, an increase of 260% in SNR was found with increasing filter sharpness. For constant dose, the SNR remained level for smooth to medium filters, then declined by up to 55% with increasing filter sharpness. For a constant reconstruction filter, the SNR increased with dose, but not as high as photon statistics would predict. However, when structured noise was removed from the noise power spectrum, the SNR did vary with quanta statistics. These results offer protocol design guidance for low-frequency-dominated objects.

A new software tool for removing, storing, and adding abnormalities to medical images for perception research studies

RATIONALE AND OBJECTIVES

Image perception studies have been difficult to perform using clinical images because of the problems associated with obtaining proven abnormalities and appropriate normal controls. The objective of this research was to develop and evaluate interactive software that allows the seamless removal, archiving and insertion of abnormal areas from computed tomography (CT) lung image sets for use in image perception research.

MATERIALS AND METHODS

The software tools for removing, archiving, and adding lesions are described in detail. The efficacy of the software to remove abnormal areas of lung CT studies was evaluated by having radiologists select the one altered image from a display of four. The software for adding lesions was evaluated by having radiologists classify displayed CT slices with lesions as real or artificial along with their confidence level.

RESULTS

Observers could not reliably detect when images had been altered by the software. In the lesion-removal experiment, the observers correctly identified the altered display in only 15.8 +/- 2.8 of 56 sets. In the lesion-add experiment, the observers correctly identified the artificially placed lesions in 38.2 +/- 3.9 of 77 sets. The frequency distribution of the correct responses did not differ from that expected from chance selection.

CONCLUSIONS

The results from both of these experiments demonstrate that radiologists could not distinguish between original and altered images. We conclude that this software can be used with volumetric CT lung images for creating normal control and target data sets for medical image perception research.

Search for lesions in mammograms: Statistical characterization of observer responses

We investigate human performance for visually detecting simulated microcalcifications and tumors embedded in x-ray mammograms as a function of signal contrast and the number of possible signal locations. Our results show that performance degradation with an increasing number of locations is well approximated by signal detection theory (SDT) with the usual Gaussian assumption. However, more stringent statistical analysis finds a departure from Gaussian assumptions for the detection of microcalcifications. We investigated whether these departures from the SDT Gaussian model could be accounted for by an increase in human internal response correlations arising from the image-pixel correlations present in 1/f spectrum backgrounds and/or observer internal response distributions that departed from the Gaussian assumption. Results were consistent with a departure from the Gaussian response distributions and suggested that the human observer internal responses were more compact than the Gaussian distribution. Finally, we conducted a free search experiment where the signal could appear anywhere within the image. Results show that human performance in a multiple-alternative forced-choice experiment can be used to predict performance in the clinically realistic free search experiment when the investigator takes into account the search area and the observers' inherent spatial imprecision to localize the targets.

Mass discrimination in mammography

RATIONALE AND OBJECTIVES

In a previous publication concerning detection of masses in mammograms it was shown that the amplitude (contrast) required for detection increased as mass size increased. The work presented here was designed to measure the variation of amplitude threshold for discrimination between masses as a function of lesion size.

MATERIALS AND METHODS

A hybrid image method with digitized masses added to digitized normal mammograms was used. The masses were extracted from surgical specimen radiographs. Observer experiments were performed using the two-alternative forced-choice method with images displayed on a computer monitor. There were two tasks: (1) discrimination between a ductal carcinoma and a fibroadenoma, and (2) discrimination between two ductal carcinomas. Masses were scaled to cover the linear size range from 1 to 16 mm. Three observers took part, two physicists and a radiologist.

RESULTS

The discrimination contrast-detail (CD) diagrams were found to have minimum threshold amplitudes at lesion sizes near 4 mm. The detection results had demonstrated an unusual contrast-detail diagram form with threshold amplitudes monotonically increasing with lesion size for lesions larger than 1 mm, which was opposite the usual result for image noise. Discrimination thresholds or masses larger than 4 mm were approximately 1.5-2 times those reported for detection of the lesions.

CONCLUSION

The detection results had been explained using a relatively simple model based on signal detection theory with some characteristics of the human visual system included. The observer model cannot explain the discrimination results, so additional complexity must be introduced to the observer model.

Flat-panel digital mammography system: contrast-detail comparison between screen-film radiographs and hard-copy images

PURPOSE

To compare the contrast-detail (CD) characteristics of screen-film (SF) and postprocessed digital images by using a phantom-based method.

MATERIALS AND METHODS

Images of a CD phantom with polymerized methyl methacrylate were acquired with SF and full-field digital mammography systems at matched exposure conditions. A four-alternative forced-choice experiment was conducted with seven observers participating in the study. Each observer was required to identify randomly located disks in phantom images from which detection curves were computed. The CD diagrams for the SF and digital systems were estimated from the detection curves and compared at 50% and 62.5% threshold levels. Furthermore, a theoretic model was used to estimate the CD performance of the SF and digital systems.

RESULTS

Analysis of covariance for mixed models was used with the natural logarithm of disk thickness as the dependent variable, the natural logarithm of disk diameter as the covariate, and the observer as a random factor. The results of statistical analysis indicated significant differences between the CD characteristics of SF and digital mammographic images at both 50% (P <.001) and 62.5% (P <.001) detection thresholds.

CONCLUSION

The authors conclude that digital CD curves, on average, exhibit threshold contrast characteristics that are lower (better) than those of SF mammography.

Observer variation and the performance accuracy gained by averaging ratings of abnormality

Six radiologists used continuous scales to rate 529 chest-film cases for likelihood of five different types of abnormalities (interstitial disease, nodule, pneumothorax, alveolar infiltrate, and rib fracture) in each of six replicated readings, yielding 36 separate ratings of each case for the five abnormalities. Separate data analyses of all cases and subsets of the difficult/subtle cases for each abnormality estimated the relative gains in accuracy (linear-scaled area below the ROC curve) obtained by averaging the case-ratings across (a) six independent replications by each reader (25% gain), (b) six different readers within each replication (34% gain), or (c) all 36 readings (48% gain). Although accuracy differed among both readers and abnormalities, ROC curves for the median ratings showed similar relative gains in accuracy, somewhat greater than those predicted from the measured rating correlations. A model for variance components in the observer's latent decision variable could predict these gains from measured correlations in the single ratings of cases. Depending on whether the model's estimates were based on realized accuracy gains or on rating correlations, about 48% or 39% of each reader's total decision variance (summed variance for positive and negative cases) consisted of random (within-reader) error that was uncorrelated between replications, another 10% or 14% came from idiosyncratic responses to individual cases, and about 43% or 47% was systematic variation that all readers found in the sampled cases.

Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds

Models of human visual detection have been successfully used in computer-generated noise. For these backgrounds, which are generally statistically stationary, model performance can be readily calculated by computing the index of detectability d' from the noise power spectrum, the signal profile, and the model template. However, model observers are ultimately needed in more real backgrounds, which may be statistically non-stationary. We investigated different methods to calculate figures of merit for model observers in real backgrounds based on different assumptions about image stationarity. We computed performance of the nonpre-whitening matched-filter observer with an eye filter on mammography and coronary angiography for an additive or a multiplicative signal. Performance was measured either by applying the model template to the images or by computing closed-form expressions with various assumptions about image stationarity. Results show first that the structured backgrounds investigated cannot be considered stationary. Second, traditional closed-form expressions of detectability calculated from the noise power spectra with the assumption of background stationarity lead to erroneous estimates of model performance. Third, the most accurate way of measuring model performances is by directly applying the model template on the images or by computing a closed-form expression that does not assume image stationarity.

Estimation of the noisy component of anatomical backgrounds

The knowledge of the relationship that links radiation dose and image quality is a prerequisite to any optimization of medical diagnostic radiology. Image quality depends, on the one hand, on the physical parameters such as contrast, resolution, and noise, and on the other hand, on characteristics of the observer that assesses the image. While the role of contrast and resolution is precisely defined and recognized, the influence of image noise is not yet fully understood. Its measurement is often based on imaging uniform test objects, even though real images contain anatomical backgrounds whose statistical nature is much different from test objects used to assess system noise. The goal of this study was to demonstrate the importance of variations in background anatomy by quantifying its effect on a series of detection tasks. Several types of mammographic backgrounds and signals were examined by psychophysical experiments in a two-alternative forced-choice detection task. According to hypotheses concerning the strategy used by the human observers, their signal to noise ratio was determined. This variable was also computed for a mathematical model based on the statistical decision theory. By comparing theoretical model and experimental results, the way that anatomical structure is perceived has been analyzed. Experiments showed that the observer's behavior was highly dependent upon both system noise and the anatomical background. The anatomy partly acts as a signal recognizable as such and partly as a pure noise that disturbs the detection process. This dual nature of the anatomy is quantified. It is shown that its effect varies according to its amplitude and the profile of the object being detected. The importance of the noisy part of the anatomy is, in some situations, much greater than the system noise. Hence, reducing the system noise by increasing the dose will not improve task performance. This observation indicates that the tradeoff between dose and image quality might be optimized by accepting a higher system noise. This could lead to a better resolution, more contrast, or less dose.

Detecting hepatic lesions: the added utility of CT liver window settings

PURPOSE

To prospectively evaluate the utility of adding computed tomographic (CT) liver windows to conventional soft-tissue windows for the detection of hepatic disease.

MATERIALS AND METHODS

One of four radiologists experienced in abdominal imaging interpreted 1,175 consecutive abdominal CT scans from one institution. Hepatic images were first interpreted by using standard soft-tissue windows. The number of lesions and confidence in lesion detection were recorded. The liver-window images were then interpreted in conjunction with the soft-tissue-window images, and the number of lesions and confidence in detection were recorded again. The proportion of patients in whom additional lesions were found by using liver windows was determined.

RESULTS

On soft-tissue-window and liver-window scans interpreted together, 869 (74%) patients had no hepatic lesions. Thirty-six (3.1%) patients had new lesions seen with the addition of liver windows. Twelve of these 36 patients had no lesions seen on soft-tissue-window scans. Twenty-six of the 36 patients with additional lesions seen had a history of neoplasm. There was a change in diagnosis in 1.7% of the patients with the addition of liver windows and a change in recommendation for follow-up in 0.85%.

CONCLUSION

Routine interpretation of liver-window scans for all abdominal CT scans has limited added utility in detecting hepatic disease.

Visual signal detection with two-component noise: low-pass spectrum effects

Detection of signals in natural images and scenes is limited by both noise and structure. The purpose of this study is to investigate phenomenological issues of signal detection in two-component noise. One component had a broadband (white) spectrum designed to simulate image noise. The other component was filtered to simulate two classes of low-pass background structure spectra: Gaussian-filtered noise and power-law noise. Measurements of human and model observer performance are reported for several aperiodic signals and both classes of background spectra. Human results are compared with two classes of observer models and are fitted very well by suboptimal prewhitening matched filter models. The nonprewhitening model with an eye filter does not agree with human results when background-noise-component power spectrum bandwidths are less than signal energy bandwidths.

Efficiency of low-contrast detail detectability in fluoroscopic imaging

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.

Enhanced displays of medical images: evaluation of the effectiveness of color, motion, and contour for detecting and localizing liver lesions

RATIONALE AND OBJECTIVES

Many perceptual studies have shown that the detection of large, low-contrast targets is better either in color or in contrast-reversing presentations than in standard gray scale. We determined the value of several new display techniques for viewing liver computed tomography (CT) scans.

METHODS

Eight observers (four radiologists and four nonradiologists) viewed sets of 100 liver CT images (50 with lesions and 50 without) under five display conditions on a Macintosh computer: (1) color (equiluminant color contrast); (2) color-luminance (combined luminance and chromatic contrast); (3) flicker (luminance contrast that reversed polarity at 2 Hz); (4) contour (shaded intensity mapping); and (5) control (conventional gray scale). Receiver operating characteristics (ROC) techniques were used for analysis.

RESULTS

The measured ROC curve areas for the different viewing conditions were as follows: control = 0.77 +/- 0.01 (mean +/- standard error of the mean); color = 0.78 +/- 0.01; color-luminance = 0.82 +/- 0.01; flicker = 0.78 +/- 0.01; and contour = 0.76 +/- 0.01. The percentage of lesions correctly located ranged from 0.82 (color-luminance) to 0.75 (flicker). Performance under the color-luminance condition was significantly better than in the control condition (p = .01), whereas the other experimental conditions were not significantly different from the control condition (p > .21).

CONCLUSION

The use of mixed color and luminance displays may have perceptual advantages for radiologists and can improve performance over that of gray-scale viewing.