{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T02:48:50Z","timestamp":1789613330511,"version":"build-2803163510"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","award":["09200576"],"award-info":[{"award-number":["09200576"]}],"id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Deep learning\u2010based computed tomography (CT) ventilation imaging (CTVI) is a promising technique for guiding functional lung avoidance radiotherapy (FLART). However, conventional approaches, which rely on anatomical CT data, may overlook important ventilation features due to the lack of motion data integration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a novel dual\u2010aware CTVI method that integrates both anatomical information from CT images and motional information from Jacobian maps to generate more accurate ventilation images for FLART.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A dataset of 66 patients with four\u2010dimensional CT (4DCT) images and reference ventilation images (RefVI) was utilized to develop the dual\u2010path fusion network (DPFN) for synthesizing ventilation images (CTVI\n                      <jats:sub>Dual<\/jats:sub>\n                      ). The DPFN model was specifically designed to integrate motion data from 4DCT\u2010generated Jacobian maps with anatomical data from average 4DCT images. The DPFN utilized two specialized feature extraction pathways, along with encoders and decoders, designed to handle both 3D average CT images and Jacobian map data. This dual\u2010processing approach enabled the comprehensive extraction of lung ventilation\u2010related features. The performance of DPFN was assessed by comparing CTVI\n                      <jats:sub>Dual<\/jats:sub>\n                      to RefVI using various metrics, including Spearman's correlation coefficients (\n                      <jats:italic>R<\/jats:italic>\n                      ), Dice similarity coefficients of high\u2010functional region (DSC\n                      <jats:sub>h<\/jats:sub>\n                      ), and low\u2010functional region (DSC\n                      <jats:sub>l<\/jats:sub>\n                      ). Additionally, CTVI\n                      <jats:sub>Dual<\/jats:sub>\n                      was benchmarked against other CTVI methods, including a dual\u2010phase CT\u2010based deep learning method (CTVI\n                      <jats:sub>DLCT<\/jats:sub>\n                      ), a radiomics\u2010based method (CTVI\n                      <jats:sub>FM<\/jats:sub>\n                      ), a super voxel\u2010based method (CTVI\n                      <jats:sub>SVD<\/jats:sub>\n                      ), a Unet\u2010based method (CTVI\n                      <jats:sub>Unet<\/jats:sub>\n                      ), and two deformable registration\u2010based methods (CTVI\n                      <jats:sub>Jac<\/jats:sub>\n                      and CTVI\n                      <jats:sub>HU<\/jats:sub>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the test group, the mean\n                      <jats:italic>R<\/jats:italic>\n                      between CTVI\n                      <jats:sub>Dual<\/jats:sub>\n                      and RefVI was 0.70, significantly outperforming CTVI\n                      <jats:sub>DLCT<\/jats:sub>\n                      (0.68), CTVI\n                      <jats:sub>FM<\/jats:sub>\n                      (0.58), CTVI\n                      <jats:sub>SVD<\/jats:sub>\n                      (0.62), and CTVI\n                      <jats:sub>Unet<\/jats:sub>\n                      (0.66), with\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05. Furthermore, the DSC\n                      <jats:sub>h<\/jats:sub>\n                      and DSC\n                      <jats:sub>l<\/jats:sub>\n                      values of CTVI\n                      <jats:sub>Dual<\/jats:sub>\n                      were 0.64 and 0.80, respectively, outperforming CTVI\n                      <jats:sub>SVD<\/jats:sub>\n                      (0.63; 0.73) and CTVI\n                      <jats:sub>Unet<\/jats:sub>\n                      (0.62; 0.77). The performance of CTVI\n                      <jats:sub>Dual<\/jats:sub>\n                      was also significantly better than that of CTVI\n                      <jats:sub>Jac<\/jats:sub>\n                      and CTVI\n                      <jats:sub>HU<\/jats:sub>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A novel dual\u2010aware CTVI model that integrates anatomical and motion information was developed to synthesize lung ventilation images. It was shown that the accuracy of lung ventilation estimation could be significantly enhanced by incorporating motional information, particularly in patients with tumor\u2010induced blockages. This approach has the potential to improve the accuracy of CTVI, enabling more effective FLART.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17466","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:59:12Z","timestamp":1729508352000},"page":"246-256","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network"],"prefix":"10.1002","volume":"52","author":[{"given":"Pei","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Kowloon Hong Kong SAR Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Kowloon Hong Kong SAR Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu\u2010Hua","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Kowloon Hong Kong SAR Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Kowloon Hong Kong SAR Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Kowloon Hong Kong SAR Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key 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