4 April 2016 On the properties of artificial neural network filters for bone-suppressed digital radiography
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Abstract
Dual-energy imaging can enhance lesion conspicuity. However, the conventional (fast kilovoltage switching) dual-shot dual-energy imaging is vulnerable to patient motion. The single-shot method requires a special design of detector system. Alternatively, single-shot bone-suppressed imaging is possible using post-image processing combined with a filter obtained from training an artificial neural network. In this study, the authors investigate the general properties of artificial neural network filters for bone-suppressed digital radiography. The filter properties are characterized in terms of various parameters such as the size of input vector, the number of hidden units, the learning rate, and so on. The preliminary result shows that the bone-suppressed image obtained from the filter, which is designed with 5,000 teaching images from a single radiograph, results in about 95% similarity with a commercial bone-enhanced image.
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Eunpyeong Park, Eunpyeong Park, Junbeom Park, Junbeom Park, Daecheon Kim, Daecheon Kim, Hanbean Youn, Hanbean Youn, Hosang Jeon, Hosang Jeon, Jin Sung Kim, Jin Sung Kim, Dong-Joong Kang, Dong-Joong Kang, Ho Kyung Kim, Ho Kyung Kim, } "On the properties of artificial neural network filters for bone-suppressed digital radiography", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97836B (4 April 2016); doi: 10.1117/12.2216739; https://doi.org/10.1117/12.2216739
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