Laboratory InvestigationA Free-response Evaluation Determining Value in the Computed Tomography Attenuation Correction Image for Revealing Pulmonary Incidental Findings: A Phantom Study
Section snippets
Image Acquisition
Because it would not be desirable from ethical and practical considerations to image enough patients in all five modalities to generate sufficient numbers of normal and abnormal cases for the observer study, a phantom study was indicated. Phantom simulation allows the production of reliable system-matched images without concerns over radiation dose.
Spherical simulated lesions with diameters 3, 5, 8, 10, and 12 mm and densities −800, −630, and +100 Hounsfield units (HU), for a total of 15
Results
Table 2 summarizes the results of the four analyses conducted (for AR = 20, 40, CT experienced, and no CT experience): it lists the F-statistic, and in parenthesis the numerator and denominator degrees of freedom, the P-value, the average number of NL marks per normal slice, the corresponding number per abnormal slice, and the average number of LL marks per abnormal slice. For 20-pixel AR and all 21 readers, Figure 2a displays the JAFROC FOMs and 95% confidence intervals for the five SPECT/CT
Discussion
This study evaluated lesion detectability in the low-resolution CT images acquired for AC as part of the SPECT/CT MPI technique. The diagnostic value of these images has been in question, but the work of Goetze et al. (14) has suggested that there is value in reporting interpretations from these images. Legislative pressures in the United Kingdom also require a formal record of each exposure to be created.
The statistically significant differences observed in this study, which were especially
Conclusions
Protocol variations in operation for CT-based AC have a significant impact on lesion-detection performance. The results imply that z-axis resolution and matrix size had the greatest impact on lesion detection, with a weaker but detectable dependence on the product of milliamp and seconds.
Acknowledgment
The authors would like to acknowledge the extensive work and development of the DLL module used in the analysis of variance at the University of Iowa by Professor Kevin Berbaum, Dr Stephen Hillis, and Dr Kevin Schartz 18, 19, 20, 21, 22, 23, 24.
We thank an anonymous referee for making several constructive suggestions on the analysis.
The authors would like to thank the University of Cumbria for the kind loan of the LUNGMAN N1 Multipurpose Chest Phantom.
Preliminary results of this study were
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Financial Disclosures: D.P.C. was supported in part by grants from the Department of Health and Human Services, National Institutes of Health, R01-EB005243 and R01-EB008688.