PURPOSE: In this study, we present a novel markerless technique, based on cone beam computed tomography (CBCT) raw projection data, to evaluate lung tumor daily motion. METHOD AND MATERIALS: The markerless technique, which uses raw CBCT projection data and locates tumors directly on every projection, consists of three steps. First, the tumor contour on the planning CT is used to create digitally reconstructed radiographs (DRRs) at every projection angle. Two sets of DRRs are created: one showing only the tumor, and another with the complete anatomy without the tumor. Second, a rigid two-dimensional image registration is performed to register the DRR set without the tumor to the CBCT projections. After the registration, the projections are subtracted from the DRRs, resulting in a projection dataset containing primarily tumor. Finally, a second registration is performed between the subtracted projection and tumor-only DRR. The methodology was evaluated using a chest phantom containing a moving tumor, and retrospectively in 4 lung cancer patients treated by stereotactic body radiation therapy. Tumors detected on projection images were compared with those from three-dimensional (3D) and four-dimensional (4D) CBCT reconstruction results. RESULTS: Results in both static and moving phantoms demonstrate that the accuracy is within 1 mm. The subsequent application to 22 sets of CBCT scan raw projection data of 4 lung cancer patients includes about 11,000 projections, with the detected tumor locations consistent with 3D and 4D CBCT reconstruction results. This technique reveals detailed lung tumor motion and provides additional information than conventional 4D images. CONCLUSION: This technique is capable of accurately characterizing lung tumor motion on a daily basis based on a conventional CBCT scan. It provides daily verification of the tumor motion to ensure that these motions are within prior estimation and covered by the treatment planning volume. Copyright Â
PURPOSE: In this study, we present a novel markerless technique, based on cone beam computed tomography (CBCT) raw projection data, to evaluate lung tumor daily motion. METHOD AND MATERIALS: The markerless technique, which uses raw CBCT projection data and locates tumors directly on every projection, consists of three steps. First, the tumor contour on the planning CT is used to create digitally reconstructed radiographs (DRRs) at every projection angle. Two sets of DRRs are created: one showing only the tumor, and another with the complete anatomy without the tumor. Second, a rigid two-dimensional image registration is performed to register the DRR set without the tumor to the CBCT projections. After the registration, the projections are subtracted from the DRRs, resulting in a projection dataset containing primarily tumor. Finally, a second registration is performed between the subtracted projection and tumor-only DRR. The methodology was evaluated using a chest phantom containing a moving tumor, and retrospectively in 4 lung cancerpatients treated by stereotactic body radiation therapy. Tumors detected on projection images were compared with those from three-dimensional (3D) and four-dimensional (4D) CBCT reconstruction results. RESULTS: Results in both static and moving phantoms demonstrate that the accuracy is within 1 mm. The subsequent application to 22 sets of CBCT scan raw projection data of 4 lung cancerpatients includes about 11,000 projections, with the detected tumor locations consistent with 3D and 4D CBCT reconstruction results. This technique reveals detailed lung tumor motion and provides additional information than conventional 4D images. CONCLUSION: This technique is capable of accurately characterizing lung tumor motion on a daily basis based on a conventional CBCT scan. It provides daily verification of the tumor motion to ensure that these motions are within prior estimation and covered by the treatment planning volume. Copyright Â
Authors: Chun-Chien Shieh; Vincent Caillet; Michelle Dunbar; Paul J Keall; Jeremy T Booth; Nicholas Hardcastle; Carol Haddad; Thomas Eade; Ilana Feain Journal: Phys Med Biol Date: 2017-03-21 Impact factor: 3.609
Authors: Wen Pei Liu; Yoshito Otake; Mahdi Azizian; Oliver J Wagner; Jonathan M Sorger; Mehran Armand; Russell H Taylor Journal: Int J Comput Assist Radiol Surg Date: 2014-12-12 Impact factor: 2.924
Authors: Marco Mueller; Per Poulsen; Rune Hansen; Wilko Verbakel; Ross Berbeco; Dianne Ferguson; Shinichiro Mori; Lei Ren; John C Roeske; Lei Wang; Pengpeng Zhang; Paul Keall Journal: Med Phys Date: 2021-12-29 Impact factor: 4.071
Authors: Maksat Haytmyradov; Hassan Mostafavi; Adam Wang; Liangjia Zhu; Murat Surucu; Rakesh Patel; Arun Ganguly; Michelle Richmond; Roberto Cassetta; Matthew M Harkenrider; John C Roeske Journal: Med Phys Date: 2019-06-01 Impact factor: 4.071
Authors: Maksat Haytmyradov; Hassan Mostafavi; Roberto Cassetta; Rakesh Patel; Murat Surucu; Liangjia Zhu; John C Roeske Journal: Med Phys Date: 2020-01-10 Impact factor: 4.071