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Northway SK, Vallejo BM, Liu L, Hansen EE, Tang S, Mah D, MacEwan IJ, Urbanic JJ, Chang C. A quantitative framework for patient-specific collision detection in proton therapy. J Appl Clin Med Phys 2024; 25:e14247. [PMID: 38131514 PMCID: PMC11005990 DOI: 10.1002/acm2.14247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Beam modifying accessories for proton therapy often need to be placed in close proximity of the patient for optimal dosimetry. However, proton treatment units are larger in size and as a result the planned treatment geometry may not be achievable due to collisions with the patient. A framework that can accurately simulate proton treatment geometry is desired. PURPOSE A quantitative framework was developed to model patient-specific proton treatment geometry, minimize air gap, and avoid collisions. METHODS The patient's external contour is converted into the International Electrotechnique Commission (IEC) gantry coordinates following the patient's orientation and each beam's gantry and table angles. All snout components are modeled by three-dimensional (3D) geometric shapes such as columns, cuboids, and frustums. Beam-specific parameters such as isocenter coordinates, snout type and extension are used to determine if any point on the external contour protrudes into the various snout components. A 3D graphical user interface is also provided to the planner to visualize the treatment geometry. In case of a collision, the framework's analytic algorithm quantifies the maximum protrusion of the external contour into the snout components. Without a collision, the framework quantifies the minimum distance of the external contour from the snout components and renders a warning if such distance is less than 5 cm. RESULTS Three different snout designs are modeled. Examples of potential collision and its aversion by snout retraction are demonstrated. Different patient orientations, including a sitting treatment position, as well as treatment plans with multiple isocenters, are successfully modeled in the framework. Finally, the dosimetric advantage of reduced air gap enabled by this framework is demonstrated by comparing plans with standard and reduced air gaps. CONCLUSION Implementation of this framework reduces incidence of collisions in the treatment room. In addition, it enables the planners to minimize the air gap and achieve better plan dosimetry.
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Affiliation(s)
- Stephen K. Northway
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
| | - Bailey M. Vallejo
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
| | - Lawrence Liu
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
| | - Emily E. Hansen
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
| | - Shikui Tang
- Texas Center for Proton TherapyIrvingTexasUSA
| | - Dennis Mah
- ProCure Proton Therapy CenterSomersetNew JerseyUSA
| | - Iain J. MacEwan
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
| | - James J. Urbanic
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
| | - Chang Chang
- Department of Radiation Medicine and Applied SciencesUniversity of California at San DiegoLa JollaCaliforniaUSA
- California Protons Cancer Therapy CenterSan DiegoCaliforniaUSA
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Yamazaki Y, Terunuma T, Kato T, Komori S, Sakae T. A novel, end-to-end framework for avoiding collisions between the patient's body and gantry in proton therapy. Med Phys 2023; 50:6684-6692. [PMID: 37816130 DOI: 10.1002/mp.16784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 08/30/2023] [Accepted: 09/28/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND Administration of external radiation therapy via proton therapy systems carries a risk of occasional collisions between the patient's body and gantry, which is increased by the snout placed near the patient for better dose distribution. Although treatment planning software (TPS) can simulate controlled collisions, the computed tomography (CT) data used for treatment planning are insufficient given that collisions can occur outside the CT imaging region. Thus, imaging the three-dimensional (3D) surface outside the CT range and combining the data with those obtained by CT are essential for avoiding collisions. PURPOSE To construct a prototype for 3D surface imaging and an end-to-end framework for preventing collisions between the patient's body and the gantry. METHODS We obtained 3D surface data using a light sectioning method (LSM). By installing only cameras in front of the CT, we achieved LSM using the CT couch motion and preinstalled patient-positioning lasers. The camera image contained both sagittal and coronal lines, which are unnecessary for LSM and were removed by deep learning. We combined LSM 3D surface data and original CT data to create synthetic Digital Imaging and Communications in Medicine (DICOM) data. Subsequently, we compared the TPS snout auto-optimization using the original CT data with the synthetic DICOM data. RESULTS The mean positional error for LSM of the arms and head was 0.7 ± 0.8 and 0.8 ± 0.8 mm for axial and sagittal imaging, respectively. The TPS snout auto-optimization indicated that the original CT data would cause collisions; however, the synthetic DICOM data prevented these collisions. CONCLUSIONS The prototype system's acquisition accuracy for 3D surface data was approximately 1 mm, which was sufficient for the collision simulation. The use of a TPS with collision avoidance can help optimize the snout position using synthetic DICOM data. Our proposed method requires no external software for collision simulation and can be integrated into the clinical workflow to improve treatment planning efficiency.
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Affiliation(s)
- Yuhei Yamazaki
- Graduate School of Comprehensive Human Science, University of Tsukuba, Tsukuba, Japan
- Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama, Japan
| | | | - Takahiro Kato
- Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama, Japan
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima, Japan
| | - Shinya Komori
- Department of Radiation Physics and Technology, Southern Tohoku BNCT Research Center, Koriyama, Japan
| | - Takeji Sakae
- Institute of Medicine, University of Tsukuba, Tsukuba, Japan
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Guyer G, Mueller S, Wyss Y, Bertholet J, Schmid R, Stampanoni MFM, Manser P, Fix MK. Technical note: A collision prediction tool using Blender. J Appl Clin Med Phys 2023; 24:e14165. [PMID: 37782250 PMCID: PMC10647990 DOI: 10.1002/acm2.14165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/26/2023] [Accepted: 09/05/2023] [Indexed: 10/03/2023] Open
Abstract
Non-coplanar radiotherapy treatment techniques on C-arm linear accelerators have the potential to reduce dose to organs-at-risk in comparison with coplanar treatment techniques. Accurately predicting possible collisions between gantry, table and patient during treatment planning is needed to ensure patient safety. We offer a freely available collision prediction tool using Blender, a free and open-source 3D computer graphics software toolset. A geometric model of a C-arm linear accelerator including a library of patient models is created inside Blender. Based on the model, collision predictions can be used both to calculate collision-free zones and to check treatment plans for collisions. The tool is validated for two setups, once with and once without a full body phantom with the same table position. For this, each gantry-table angle combination with a 2° resolution is manually checked for collision interlocks at a TrueBeam system and compared to simulated collision predictions. For the collision check of a treatment plan, the tool outputs the minimal distance between the gantry, table and patient model and a video of the movement of the gantry and table, which is demonstrated for one use case. A graphical user interface allows user-friendly input of the table and patient specification for the collision prediction tool. The validation resulted in a true positive rate of 100%, which is the rate between the number of correctly predicted collision gantry-table combinations and the number of all measured collision gantry-table combinations, and a true negative rate of 89%, which is the ratio between the number of correctly predicted collision-free combinations and the number of all measured collision-free combinations. A collision prediction tool is successfully created and able to produce maps of collision-free zones and to test treatment plans for collisions including visualisation of the gantry and table movement.
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Affiliation(s)
- Gian Guyer
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
| | - Silvan Mueller
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
| | - Yanick Wyss
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
| | - Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
| | - Remo Schmid
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
| | | | - Peter Manser
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
| | - Michael K. Fix
- Division of Medical Radiation Physics and Department of Radiation OncologyInselspitalBern University Hospital, and University of BernSwitzerland
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Northway C, Lincoln JD, Little B, Syme A, Thomas CG. Patient-Specific Collision Zones for 4π Trajectory Optimized Radiation Therapy. Med Phys 2022; 49:1407-1416. [PMID: 35023581 DOI: 10.1002/mp.15452] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/19/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The 4π methodology determines optimized non-coplanar sub arcs for stereotactic radiation therapy which minimize dose to organs-at-risk. Every combination of treatment angle is examined, but some angles are not appropriate as a collision would occur between the gantry and the couch or the gantry and the patient. Those combinations of couch and gantry angles are referred to as collision zones. A major barrier to applying 4π to stereotactic body radiation therapy (SBRT) is the unknown shape of the collision zones, which are significant as patients take up a large volume within the 4π sphere. This study presents a system which determines patient-specific collision zones, without additional clinical steps, to enable safe and deliverable non-coplanar treatment trajectories for SBRT patients. METHODS To augment patient's computed tomography (CT) scan, full body scans of patients in treatment position were acquired using an optical scanner. A library of a priori scans (N = 25) was created. Based on the patients treatment position and their body dimensions, a library scan is selected and registered to the CT scan of the patient. Next, a model of the couch and immobilization equipment is added to the patient model. This results in a patient model that is then aligned with a model of the treatment linac in a "virtual treatment room", where both components can be rotated to test for collisions. To test the collision detection algorithm, an end-to-end test was performed using a cranial phantom. The registration algorithm was tested by comparing the registered patient collision zones to those generated by using the patient's matching scan. RESULTS The collision detection algorithm was found to have a 97.80% accuracy, a 99.99% sensitivity and a 99.99% negative predictive value (NPV). Analysis of the registration algorithm determined that a 6 cm buffer was required to achieve a 99.65% mean sensitivity, where a sensitivity of unity is considered to be a requirement for safe treatment delivery. With a 6 cm buffer the mean accuracy was 86.70% and the mean NPV was 99.33%. CONCLUSIONS Our method of determining patient-specific collision zones can be accomplished with minimal user intervention based on an a priori library of body surface scans, thus enabling the safe application of 4π SBRT.
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Affiliation(s)
- Cassidy Northway
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.,Author's present intuition is Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - John David Lincoln
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
| | - Brian Little
- Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada
| | - Alasdair Syme
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.,Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada.,Department of Radiation Oncology, Dalhousie University, Halifax, NS, Canada.,Beatrice Hunter Cancer Research Institute, Halifax, NS, Canada
| | - Christopher G Thomas
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.,Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada.,Department of Radiation Oncology, Dalhousie University, Halifax, NS, Canada.,Beatrice Hunter Cancer Research Institute, Halifax, NS, Canada.,Department of Radiology, Dalhousie University, Halifax, NS, Canada
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Dougherty JM, Whitaker TJ, Mundy DW, Tryggestad EJ, Beltran CJ. Design of a 3D patient-specific collision avoidance virtual framework for half-gantry proton therapy system. J Appl Clin Med Phys 2021; 23:e13496. [PMID: 34890094 PMCID: PMC8833276 DOI: 10.1002/acm2.13496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/20/2021] [Accepted: 11/14/2021] [Indexed: 11/24/2022] Open
Abstract
Introduction This study presents a comprehensive collision avoidance framework based on three‐dimension (3D) computer‐aided design (CAD) modeling, a graphical user interface (GUI) as peripheral to the radiation treatment planning (RTP) environment, and patient‐specific plan parameters for intensity‐modulated proton therapy (IMPT). Methods A stand‐alone software application was developed leveraging the Varian scripting application programming interface (API) for RTP database object accessibility. The Collision Avoider software models the Hitachi ProBeat‐V half gantry design and the Kuka robotic couch with triangle mesh structures. Patient‐specific plan parameters are displayed in the collision avoidance software for potential proximity evaluation. The external surfaces of the patients and the immobilization devices are contoured based on computed tomography (CT) images. A “table junction‐to‐CT‐origin” (JCT) measurement is made for every patient at the time of CT simulation to accurately provide reference location of the patient contours to the treatment couch. Collision evaluations were performed virtually with the program during treatment planning to prevent four major types of collisional events: collisions between the gantry head and the treatment couch, gantry head and the patient's body, gantry head and the robotic arm, and collisions between the gantry head and the immobilization devices. Results The Collision Avoider software was able to accurately model the proton treatment delivery system and the robotic couch position. Commonly employed clinical beam configuration and JCT values were investigated. Brain and head and neck patients require more complex gantry and patient positioning system configurations. Physical measurements were performed to validate 3D CAD model geometry. Twelve clinical proton treatment plans were used to validate the accuracy of the software. The software can predict all four types of collisional events in our clinic since its full implementation in 2020. Conclusion A highly efficient patient‐specific collision prevention program for scanning proton therapy has been successfully implemented. The graphical program has provided accurate collision detection since its inception at our institution.
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Affiliation(s)
- Jingjing M Dougherty
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA.,Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas J Whitaker
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, Texas, USA
| | - Daniel W Mundy
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Erik J Tryggestad
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Chris J Beltran
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA
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Batista V, Meyer J, Kügele M, Al-Hallaq H. Clinical paradigms and challenges in surface guided radiation therapy: Where do we go from here? Radiother Oncol 2020; 153:34-42. [PMID: 32987044 DOI: 10.1016/j.radonc.2020.09.041] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/26/2022]
Abstract
Surface guided radiotherapy (SGRT) is becoming a routine tool for patient positioning for specific clinical sites in many clinics. However, it has not yet gained its full potential in terms of widespread adoption. This vision paper first examines some of the difficulties in transitioning to SGRT before exploring the current and future role of SGRT alongside and in concert with other imaging techniques. Finally, future horizons and innovative ideas that may shape and impact the direction of SGRT going forward are reviewed.
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Affiliation(s)
- Vania Batista
- Department of Radiation Oncology, Heidelberg University Hospital, Germany; Heidelberg Institute of Radiation Oncology (HIRO), Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany.
| | - Juergen Meyer
- Seattle Cancer Care Alliance, University of Washington, Department of Radiation Oncology, United States.
| | - Malin Kügele
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden; Medical Radiation Physics, Department of Clinical Sciences, Lund University, Sweden.
| | - Hania Al-Hallaq
- The University of Chicago, Department of Radiation and Cellular Oncology, United States.
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