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.
Single-shot dual-energy sandwich detector can produce sharp images because of subtraction of images from two sub-detector layers, which have different thick x-ray converters, of the sandwich detector. Inspired by this observation, the authors have developed a microtomography system with the sandwich detector in pursuit of high-resolution bone-enhanced small-animal imaging. The preliminary results show that the bone-enhanced images reconstructed with the subtracted projection data are better in visibility of bone details than the conventionally reconstructed images. In addition, the bone-enhanced images obtained from the sandwich detector are relatively immune to the artifacts caused by photon starvation. The microtomography with the single-shot dual-energy sandwich detector will be useful for the high-resolution bone imaging.