https://scholars.lib.ntu.edu.tw/handle/123456789/553985
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Lee, Y.-T. | en_US |
dc.contributor.author | Tsou, C.-H. | en_US |
dc.contributor.author | YEUN-CHUNG CHANG | en_US |
dc.contributor.author | CHUNG-MING CHEN | en_US |
dc.creator | Lee Y.-T.;Tsou C.-H.;Yeun-Chung Chang;Chen C.-M. | - |
dc.date.accessioned | 2021-03-19T06:57:38Z | - |
dc.date.available | 2021-03-19T06:57:38Z | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 1605-7422 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/553985 | - |
dc.description.abstract | Perfusion computed tomography (CT) has been widely used to assess the response of lung cancer treatment. However, the respiratory motion has become the major obstacle to the pixel-based time-series analyses. To minimize the effect of respiratory motion and investigate the feasibility of perfusion CT for prediction of tumor response and prognosis of non- small cell lung cancer, an image registration framework is proposed by unifying a virtual 3D local rigid alignment and 3D global non-rigid alignment. The basic idea is to use the perfusion CT data and routine whole-lung CT data, respectively. To realize this idea, maximum intensity projection (MIP) of the time series perfusion CT images is first generated, followed by decomposing the MIP image into region of interest (ROI), which is located on a lung nodule. For the ROI, affine transformation model based on mutual information is performed to estimate the virtual three dimensional linear deformations. Following that, the 3D thin plate spline (TPS) is carried out to establish the pixel correspondence between the paired volumetric CT data. The control points for the TPS are global feature points chosen from the boundary of whole lung, which are automatically derived by using the iterative closest point (ICP) matching Algorithm. The proposed algorithm has been evaluated both qualitatively and quantitatively on real lung perfusion CT datasets. From the time-intensity curves and perfusion parameters, the experiment results suggest that the findings on perfusion CT images obtained after treatment may be considered as a significant predictor of lung cancer. ? 2013 SPIE. | - |
dc.relation.ispartof | Progress in Biomedical Optics and Imaging - Proceedings of SPIE | - |
dc.subject.classification | [SDGs]SDG3 | - |
dc.subject.other | Lung tumor; Non-rigid; Perfusion analysis; Perfusion-ct; Rigid; Algorithms; Biological organs; Diagnosis; Diseases; Image matching; Image registration; Medical applications; Molecular imaging; Pixels; Respiratory mechanics; Three dimensional; Three dimensional computer graphics; Tumors; Computerized tomography | - |
dc.title | An automated method for registration and perfusion analysis of pulmonary CT data for evaluating response to radiotherapy in patient with non small cell lung cancer | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1117/12.2006741 | - |
dc.identifier.scopus | 2-s2.0-84878286809 | - |
dc.relation.journalvolume | 8672 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | Radiology | - |
crisitem.author.dept | Medical Imaging-NTUH | - |
crisitem.author.dept | Biomedical Engineering | - |
crisitem.author.orcid | 0000-0001-9984-5713 | - |
crisitem.author.orcid | 0000-0002-0023-5817 | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | College of Engineering | - |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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