Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study
A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32539 |
| الوسوم: |
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| _version_ | 1864513437944512512 |
|---|---|
| author | Dhou, Salam |
| author2 | Alkhodari, Mohanad Ionascu, Dan Williams, Christopher Lewis, John H. |
| author2_role | author author author author |
| author_facet | Dhou, Salam Alkhodari, Mohanad Ionascu, Dan Williams, Christopher Lewis, John H. |
| author_role | author |
| dc.creator.none.fl_str_mv | Dhou, Salam Alkhodari, Mohanad Ionascu, Dan Williams, Christopher Lewis, John H. |
| dc.date.none.fl_str_mv | 2022-01-18 2025-12-08T10:47:26Z 2025-12-08T10:47:26Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Dhou, S., Alkhodari, M., Ionascu, D., Williams, C., & Lewis, J. H. (2022). Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study. Journal of Imaging, 8(2), 17. https://doi.org/10.3390/jimaging8020017 2313-433X https://hdl.handle.net/11073/32539 10.3390/jimaging8020017 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/jimaging8020017 |
| dc.rights.none.fl_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.subject.none.fl_str_mv | Principal Component Analysis (PCA) Motion Model Respiratory-Correlated Four-Dimensional Cone-Beam CT (4D-CBCT) Lung Cancer Stereotactic Body Radiotherapy (SBRT) Image-Guided Radiation Therapy (IGRT) |
| dc.title.none.fl_str_mv | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| dc.type.none.fl_str_mv | Published version Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior–inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment. |
| format | article |
| id | aus_60ee02cd55be404b2c2b6062e6264cf7 |
| identifier_str_mv | Dhou, S., Alkhodari, M., Ionascu, D., Williams, C., & Lewis, J. H. (2022). Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study. Journal of Imaging, 8(2), 17. https://doi.org/10.3390/jimaging8020017 2313-433X 10.3390/jimaging8020017 |
| language_invalid_str_mv | en |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32539 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| spelling | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility StudyDhou, SalamAlkhodari, MohanadIonascu, DanWilliams, ChristopherLewis, John H.Principal Component Analysis (PCA)Motion ModelRespiratory-Correlated Four-Dimensional Cone-Beam CT (4D-CBCT)Lung CancerStereotactic Body Radiotherapy (SBRT)Image-Guided Radiation Therapy (IGRT)A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior–inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.American University of SharjahMDPI2025-12-08T10:47:26Z2025-12-08T10:47:26Z2022-01-18Published versionPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfDhou, S., Alkhodari, M., Ionascu, D., Williams, C., & Lewis, J. H. (2022). Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study. Journal of Imaging, 8(2), 17. https://doi.org/10.3390/jimaging80200172313-433Xhttps://hdl.handle.net/11073/3253910.3390/jimaging8020017enhttps://doi.org/10.3390/jimaging8020017Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/oai:repository.aus.edu:11073/325392025-12-08T11:47:52Z |
| spellingShingle | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study Dhou, Salam Principal Component Analysis (PCA) Motion Model Respiratory-Correlated Four-Dimensional Cone-Beam CT (4D-CBCT) Lung Cancer Stereotactic Body Radiotherapy (SBRT) Image-Guided Radiation Therapy (IGRT) |
| status_str | publishedVersion |
| title | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| title_full | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| title_fullStr | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| title_full_unstemmed | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| title_short | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| title_sort | Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study |
| topic | Principal Component Analysis (PCA) Motion Model Respiratory-Correlated Four-Dimensional Cone-Beam CT (4D-CBCT) Lung Cancer Stereotactic Body Radiotherapy (SBRT) Image-Guided Radiation Therapy (IGRT) |
| url | https://hdl.handle.net/11073/32539 |