Improving Image Quality of Medical Low-Dose X-ray Image Sequences Using a Neural Filter
Kenji Suzuki, Tatsuya Hayashi, Shigeyuki Ikeda, Isao Horiba, Noboru Sugie, Michio Nanki · IEEJ Transactions on Electronics Information and Systems · 1999
In this paper, we propose a novel dynamic filter using a multilayer neural network, called the neural filter with spatiotemporal inputs (NFST), for improving image quality of medical low-dose X-ray image sequences. We model the noise in the medical X-ray image sequences to make simulated low-dose X-ray images for training the NFST, and have trained the NFST using them. We have performed experiments on the NFST with not only the simulated low-dose X-ray image sequences but also real low-dose X-ray image sequences. Experimental results have demonstrated that the NFST can effectively remove the actual noise in the medical X-ray image sequences, and the ability of NFST is superior to that of the conventional dynamic filter by the quantitative evaluation of image quality. We expect that medical X-ray systems reduce dosage to the patients effectively using it.