{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:12:08Z","timestamp":1790392328698,"version":"4.1.0"},"reference-count":14,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV\/MV image pair.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed model is a combination of 2D and 3D networks. The proposed model consists of five major parts: (1) kV and MV feature extractors are used to extract deep features from the perpendicular kV and MV projections. (2) The feature\u2010matching step is used to re\u2010align the feature maps to their projection angle in a Cartesian coordinate system. By using a residual module, the feature map can focus more on the difference between the estimated and ground truth images. (3) In addition, the feature map is downsized to include more global semantic information for the 3D estimation, which is useful to reduce inhomogeneity. By using convolution\u2010based reweighting, the model is able to further increase the uniformity of image. (4) To reduce the blurry noise of generated 3D volume, the Laplacian latent space loss calculated via the feature map that is extracted via specifically\u2010learned Gaussian kernel is used to supervise the network. (5) Finally, the 3D volume is derived from the trained model. We conducted a proof\u2010of\u2010concept study using 50 patients with lung cancer. An orthogonal kV\/MV pair was generated by ray tracing through CT of each phase in a 4D CT scan. Orthogonal kV\/MV pairs from nine respiratory phases were used to train this patient\u2010specific model while the kV\/MV pair of the remaining phase was held for model testing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results are based on simulation data and phantom results from a real Linac system. The mean absolute error (MAE) values achieved by our method were 57.5 HU and 77.4 HU within body and tumor region\u2010of\u2010interest (ROI), respectively. The mean achieved peak\u2010signal\u2010to\u2010noise ratios (PSNR) were 27.6\u00a0dB and 19.2\u00a0dB within the body and tumor ROI, respectively. The achieved mean normalized cross correlation (NCC) values were 0.97 and 0.94 within the body and tumor ROI, respectively. A phantom study demonstrated that the proposed method can accurately re\u2010position the phantom after shift. It is also shown that the proposed method using both kV and MV is superior to current method using kV or MV only in image quality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>These results demonstrate the feasibility and accuracy of our proposed fast volumetric imaging method from an orthogonal kV\/MV pair, which provides a potential solution for daily treatment setup and verification of patients receiving radiation therapy for lung cancer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16377","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T14:04:43Z","timestamp":1679321083000},"page":"5518-5527","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning\u2010based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study"],"prefix":"10.1002","volume":"50","author":[{"given":"Yang","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"},{"name":"Department of Radiation and Cellular Oncology University of Chicago  Chicago USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonghe","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Roper","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqiao","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aparna H.","family":"Kesarwala","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin","family":"Higgins","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey D.","family":"Bradley","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"},{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"SinhaA SugawaraY HiranoY GA\u2010GAN: CT reconstruction from biplanar DRRs using GAN with Guided Attention. arXiv preprint arXiv:190912525.2019."},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"YingX GuoH MaK WuJ WengZ ZhengY X2CT\u2010GAN: reconstructing CT from biplanar X\u2010rays with generative adversarial networks.Paper presented at: proceedings of the IEEE conference on computer vision and pattern recognition2019.","DOI":"10.1109\/CVPR.2019.01087"},{"key":"e_1_2_8_4_1","unstructured":"KastenY DoktofskyD KovlerI End\u2010To\u2010End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi\u2010Planar X\u2010Ray Images. arXiv preprint arXiv:200400871.2020."},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3426002"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/6\/2372"},{"issue":"3","key":"e_1_2_8_7_1","first-page":"449","article-title":"Single\u2010projection based volumetric image reconstruction and 3D tumor localization in real time for lung cancer radiotherapy [published online ahead of print 2010\/10\/01]","volume":"13","author":"Li R","year":"2010","journal-title":"Med Image Comput Comput Assist 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