28 March 2007 A mathematical model approach toward combining information from multiple image projections of the same patient
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In this study, we used a mathematical observer model to combine information obtained from multiple angular projections of the same breast to determine the overall detection performance of a multi-projection breast imaging system in detectability of a simulated mass. 82 subjects participated in the study and 25 angular projections of each breast were acquired. Projections from a simulated 3 mm 3-D lesion were added to the projection images. The lesion was assumed to be embedded in the compressed breast at a distance of 3 cm from the detector. Hotelling observer with Laguerre-Gauss channels (LG CHO) was applied to each image. Detectability was analyzed in terms of ROC curves and the area under ROC curves (AUC). The critical question studied is how to best integrate the individual decision variables across multiple (correlated) views. Towards that end, three different methods were investigated. Specifically, 1) ROCs from different projections were simply averaged; 2) the test statistics from different projections were averaged; and 3) a Bayesian decision fusion rule was used. Finally, AUC of the combined ROC was used as a parameter to optimize the acquisition parameters to maximize the performance of the system. It was found that the Bayesian decision fusion technique performs better than the other two techniques and likely offers the best approximation of the diagnostic process. Furthermore, if the total dose level is held constant at 1/25th of dual-view mammographic screening dose, the highest detectability performance is observed when considering only two projections spread along an angular span of 11.4°.
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Amarpreet S. Chawla, Ehsan Samei, Craig Abbey, "A mathematical model approach toward combining information from multiple image projections of the same patient", Proc. SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 65101K (28 March 2007); doi: 10.1117/12.713651; https://doi.org/10.1117/12.713651

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