The effect of experience on detectability in local area anatomical noise

Detection of radioactive fragments in patients after radiological or nuclear emergencies using computed tomography and digital radiography

A comparison has been carried out between standard-dose computed tomography, non-diagnostic computed tomography and digital radiography with respect to their suitability for detecting radioactive fragments associated with nuclear or radiological events such as debris from radiological dispersal devices. The purpose was to investigate if radiographic imaging is justified for the detection and localisation of radioactive fragments in affected patients. Fragments of uranium (U), copper (Cu), iron (Fe) and volcanic ash with effective diameters ranging from (approximately) 100 to 700 μm were selected. The fragments were positioned at two different locations on an anatomical torso phantom and images were produced with standard-dose CT, non-diagnostic CT and digital radiography. Capsules with radionuclides of (137)Cs, (60)Co and (99m)Tc were also positioned in the phantom and the effective doses were estimated for radionuclide exposures as well as for standard-dose CT, non-diagnostic CT and digital radiography. For standard-dose CT and digital radiography, U, Cu and Fe fragments were detected in sizes down to 100-180, 250-300 and 300-400 μm respectively. For the non-diagnostic CT the results were 180-250 μm (for U), 300-400 μm (for Cu) and 400-500 μm (for Fe). The effective dose from the standard-dose CT, non-diagnostic CT and digital radiography was 5.6, 1.9 and 0.76 mSv. Corresponding doses from (137)Co, (60)Co and (99m)Tc positioned at the site of fragments were in the range of 0.07-0.1, 0.32-0.45 and 0.08-0.09 mSv per MBq during 24 h. We conclude that, for a number of gamma emitters with activity levels on the order of magnitude of megabecquerel, imaging using ionising radiation can be justified since the effective dose from the radionuclides will exceed the dose from the radiological examination.

Grey-scale inversion improves detection of lung nodules

Objective The current study aims to establish whether detection of solitary pulmonary nodules can be improved by inverting the grey scale of posteroanterior (PA) chests. Methods 30 PA chest images were presented on 2 occasions to 16 senior radiologists on either primary or secondary class displays in the standard or inverted mode. 15 images within each group contained a single nodule positioned in a range of anatomical sites. A receiver operating characteristic (ROC) methodology was used to explore differences between the presentation modes. Results Improved ROC scores were evident with inverted (Az 0.77) compared with standard (Az 0.73) (p=0.02) images; however, this difference was seen only with the primary displays. The benefits seen are most likely owing to increased nodule luminance with the inverted images, particularly when using primary displays. Conclusion This study demonstrates that the inverted image can offer advantages in lung nodule detection over the standard presentation mode when images are viewed on high-specification viewing systems. The study has demonstrated that there is an improvement in the detectability of lung nodules on an inverted image with a primary display monitor that is not evident with secondary displays. This is likely to be the result of increased nodule luminance on primary displays when images are presented in the inverted mode.

Grey-scale inversion improves detection of lung nodules

OBJECTIVE

The current study aims to establish whether detection of solitary pulmonary nodules can be improved by inverting the grey scale of posteroanterior (PA) chests.

METHODS

30 PA chest images were presented on 2 occasions to 16 senior radiologists on either primary or secondary class displays in the standard or inverted mode. 15 images within each group contained a single nodule positioned in a range of anatomical sites. A receiver operating characteristic (ROC) methodology was used to explore differences between the presentation modes.

RESULTS

Improved ROC scores were evident with inverted (Az 0.77) compared with standard (Az 0.73) (p=0.02) images; however, this difference was seen only with the primary displays. The benefits seen are most likely owing to increased nodule luminance with the inverted images, particularly when using primary displays.

CONCLUSION

This study demonstrates that the inverted image can offer advantages in lung nodule detection over the standard presentation mode when images are viewed on high-specification viewing systems. The study has demonstrated that there is an improvement in the detectability of lung nodules on an inverted image with a primary display monitor that is not evident with secondary displays. This is likely to be the result of increased nodule luminance on primary displays when images are presented in the inverted mode.

Mass detection on mammograms: influence of signal shape uncertainty on human and model observers

We studied the influence of signal variability on human and model observers for detection tasks with realistic simulated masses superimposed on real patient mammographic backgrounds and synthesized mammographic backgrounds (clustered lumpy backgrounds, CLB). Results under the signal-known-exactly (SKE) paradigm were compared with signal-known-statistically (SKS) tasks for which the observers did not have prior knowledge of the shape or size of the signal. Human observers' performance did not vary significantly when benign masses were superimposed on real images or on CLB. Uncertainty and variability in signal shape did not degrade human performance significantly compared with the SKE task, while variability in signal size did. Implementation of appropriate internal noise components allowed the fit of model observers to human performance.

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.