@article{oai:repo.qst.go.jp:00074617,
 author = {平井, 隆介 and 坂田, 幸辰 and 森, 慎一郎 and Hirai, Ryusuke and Sakata, Yukinobu and Mori, Shinichiro},
 journal = {Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)},
 month = {Mar},
 note = {Purpose: To improve respiratory gating accuracy and treatment throughput, we developed a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN).
Methods: In the learning stage, target positions were projected onto digitally reconstructed radiography (DRR) images from four-dimensional computed tomography (4DCT). DRR images were cropped into subimages of the target or surrounding regions to build a network that takes input of the image pattern of subimages and produces a target probability map (TPM) for estimating the target position. Using multiple subimages, a DNN was trained to generate a TPM based on the target position projected onto the DRRs. In the tracking stage, the network takes in the subimages cropped from fluoroscopic images at the same position of the subimages on the DRRs and produces TPMs, which are used to estimate target positions. We integrated the lateral correction to modify an estimated target position by using a linear regression model. We tracked five lung and five liver cases, and calculated tracking accuracy (Euclidian distance in 3D space) by subtracting the estimated position from the reference.
Results: Tracking accuracy averaged over all patients was 1.64 ± 0.73 mm. Accuracy for liver cases (1.37 ± 0.81 mm) was better than that for lung cases (1.90 ± 0.65 mm). Computation time was < 40 ms for a pair of fluoroscopic images.
Conclusions: Our markerless tracking algorithm successfully estimated tumor positions. We believe our results will provide useful information to advance tumor tracking technology.},
 pages = {22--29},
 title = {Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis},
 volume = {50},
 year = {2019}
}