In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks
Respiratory-Correlated cone beam computed tomography (4D-CBCT) is an emerging image-guided radiation therapy (IGRT) technique that is used to account for the uncertainties caused by respiratory-induced motion in the radiotherapy treatment of tumors in thoracic and upper-abdomen regions. In 4D-CBCT,...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/23580 |
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| _version_ | 1864513434005012480 |
|---|---|
| author | Dweek, Samaa |
| author2 | Dhou, Salam Shanableh, Tamer |
| author2_role | author author |
| author_facet | Dweek, Samaa Dhou, Salam Shanableh, Tamer |
| author_role | author |
| dc.creator.none.fl_str_mv | Dweek, Samaa Dhou, Salam Shanableh, Tamer |
| dc.date.none.fl_str_mv | 2022-04-14T09:10:24Z 2022-04-14T09:10:24Z 2022 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Samaa Dweek, Salam Dhou, and Tamer Shanableh "In-between projection interpolation in cone-beam CT imaging using convolutional neural networks", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203129 (4 April 2022); https://doi.org/10.1117/12.2611474 http://hdl.handle.net/11073/23580 10.1117/12.2611474 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Society of Photo-Optical Instrumentation Engineers (SPIE) |
| dc.relation.none.fl_str_mv | https://doi.org/10.1117/12.2611474 |
| dc.subject.none.fl_str_mv | Image-guided radiation therapy 4D cone-beam CT (CBCT) Respiratory motion Image interpolation Convolutional neural networks (CNNs) Deep learning |
| dc.title.none.fl_str_mv | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| dc.type.none.fl_str_mv | Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Respiratory-Correlated cone beam computed tomography (4D-CBCT) is an emerging image-guided radiation therapy (IGRT) technique that is used to account for the uncertainties caused by respiratory-induced motion in the radiotherapy treatment of tumors in thoracic and upper-abdomen regions. In 4D-CBCT, projections are sorted into bins based on their respiratory phase and a 3D image is reconstructed from each bin. However, the quality of the resulting 4D-CBCT images is limited by the streaking artifacts that result from having an insufficient number of projections in each bin. In this work, an interpolation method based on Convolutional Neural Networks (CNN) is proposed to generate new in-between projections to increase the overall number of projections used in 4D-CBCT reconstruction. Projections simulated using XCAT phantom were used to assess the proposed method. The interpolated projections using the proposed method were compared to the corresponding original projections by calculating the peak-signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index measurement (SSIM). Moreover, the results of the proposed method were compared to the results of existing standard interpolation methods, namely, linear, spline, and registration-based methods. The interpolated projections using the proposed method had an average PSNR, RMSE, and SSIM of 35.939, 4.115, and 0.968, respectively. Moreover, the results achieved by the proposed method surpassed the results achieved by the existing interpolation methods tested on the same dataset. In summary, this work demonstrates the feasibility of using CNN-based methods in generating in-between projections and shows a potential advantage to 4D-CBCT reconstruction. |
| format | article |
| id | aus_a96e8d34f9eb93be6651fa2a4c2cfb3a |
| identifier_str_mv | Samaa Dweek, Salam Dhou, and Tamer Shanableh "In-between projection interpolation in cone-beam CT imaging using convolutional neural networks", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203129 (4 April 2022); https://doi.org/10.1117/12.2611474 10.1117/12.2611474 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/23580 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Society of Photo-Optical Instrumentation Engineers (SPIE) |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural NetworksDweek, SamaaDhou, SalamShanableh, TamerImage-guided radiation therapy4D cone-beam CT (CBCT)Respiratory motionImage interpolationConvolutional neural networks (CNNs)Deep learningRespiratory-Correlated cone beam computed tomography (4D-CBCT) is an emerging image-guided radiation therapy (IGRT) technique that is used to account for the uncertainties caused by respiratory-induced motion in the radiotherapy treatment of tumors in thoracic and upper-abdomen regions. In 4D-CBCT, projections are sorted into bins based on their respiratory phase and a 3D image is reconstructed from each bin. However, the quality of the resulting 4D-CBCT images is limited by the streaking artifacts that result from having an insufficient number of projections in each bin. In this work, an interpolation method based on Convolutional Neural Networks (CNN) is proposed to generate new in-between projections to increase the overall number of projections used in 4D-CBCT reconstruction. Projections simulated using XCAT phantom were used to assess the proposed method. The interpolated projections using the proposed method were compared to the corresponding original projections by calculating the peak-signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index measurement (SSIM). Moreover, the results of the proposed method were compared to the results of existing standard interpolation methods, namely, linear, spline, and registration-based methods. The interpolated projections using the proposed method had an average PSNR, RMSE, and SSIM of 35.939, 4.115, and 0.968, respectively. Moreover, the results achieved by the proposed method surpassed the results achieved by the existing interpolation methods tested on the same dataset. In summary, this work demonstrates the feasibility of using CNN-based methods in generating in-between projections and shows a potential advantage to 4D-CBCT reconstruction.Society of Photo-Optical Instrumentation Engineers (SPIE)2022-04-14T09:10:24Z2022-04-14T09:10:24Z2022Postprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSamaa Dweek, Salam Dhou, and Tamer Shanableh "In-between projection interpolation in cone-beam CT imaging using convolutional neural networks", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203129 (4 April 2022); https://doi.org/10.1117/12.2611474http://hdl.handle.net/11073/2358010.1117/12.2611474en_UShttps://doi.org/10.1117/12.2611474oai:repository.aus.edu:11073/235802024-08-22T12:07:38Z |
| spellingShingle | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks Dweek, Samaa Image-guided radiation therapy 4D cone-beam CT (CBCT) Respiratory motion Image interpolation Convolutional neural networks (CNNs) Deep learning |
| status_str | publishedVersion |
| title | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| title_full | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| title_fullStr | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| title_full_unstemmed | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| title_short | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| title_sort | In-Between Projection Interpolation in Cone-Beam CT Imaging using Convolutional Neural Networks |
| topic | Image-guided radiation therapy 4D cone-beam CT (CBCT) Respiratory motion Image interpolation Convolutional neural networks (CNNs) Deep learning |
| url | http://hdl.handle.net/11073/23580 |