Collision prediction for intracranial stereotactic radiosurgery planning: An easy-to-implement analytical solution

Felefly, Tony; Achkar, S.; Khater, N.; Sayah, R.; Fares, Georges; Farah, N.; Barouky, J.; Azoury, F.; Khoury, C. El; Roukoz, C.; Nasr, D. Nehme; Nasr, Elie

doi:10.1016/j.canrad.2020.01.003

Cited by 2 publications

(5 citation statements)

References 18 publications

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“…Numerous collision detection frameworks using different methodologies have been reported for both photon and proton external beam therapy 17–30 . In general, the treatment head (gantry) and the patient support (table or chair) are modeled with varying degrees of details, that is, from a simple set of connected vertices to actual computer‐aided design files 18,26,27,31 .…”

Section: Introductionmentioning

confidence: 99%

“…In general, the treatment head (gantry) and the patient support (table or chair) are modeled with varying degrees of details, that is, from a simple set of connected vertices to actual computer‐aided design files 18,26,27,31 . The patient has also been represented by a simple geometry like an ellipse 17 or a column, 32,33 or skipped for simplicity 18,19,23,28 . Some frameworks use a phantom or an averaged patient to establish a lookup table of “collision zones” to be excluded when planning 21,27,34,35 .…”

Section: Introductionmentioning

confidence: 99%

“…Numerous collision detection frameworks using different methodologies have been reported for both photon and proton external beam therapy. 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 In general, the treatment head (gantry) and the patient support (table or chair) are modeled with varying degrees of details, that is, from a simple set of connected vertices to actual computer‐aided design files. 18 , 26 , 27 , 31 The patient has also been represented by a simple geometry like an ellipse 17 or a column, 32 , 33 or skipped for simplicity.…”

Section: Introductionmentioning

confidence: 99%

“… 18 , 26 , 27 , 31 The patient has also been represented by a simple geometry like an ellipse 17 or a column, 32 , 33 or skipped for simplicity. 18 , 19 , 23 , 28 Some frameworks use a phantom or an averaged patient to establish a lookup table of “collision zones” to be excluded when planning. 21 , 27 , 34 , 35 Patient‐specific contours, generated either by computed tomography (CT) or surface imaging cameras, 27 , 32 , 35 , 36 , 37 have also been used where sophisticated numerical methods such as ray‐tracing or mesh triangulations are required for collision detection.…”

Section: Introductionmentioning

confidence: 99%

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A quantitative framework for patient‐specific collision detection in proton therapy

Northway,

Vallejo,

Liu

et al. 2023

J Applied Clin Med Phys

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BackgroundBeam 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.PurposeA quantitative framework was developed to model patient‐specific proton treatment geometry, minimize air gap, and avoid collisions.MethodsThe 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.ResultsThree 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.ConclusionImplementation 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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A quantitative framework for patient‐specific collision detection in proton therapy

Northway,

Vallejo,

Liu

et al. 2023

J Applied Clin Med Phys

View full text Add to dashboard Cite

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“…First, vendor provided software modules, either based on a simple treatment machine geometry modeling or a more sophisticated three‐dimension (3D) computer‐aided design (CAD) modeling. The second class of solutions is a group of user‐initiated, practical in‐house techniques including: mathematical approximation of collision‐free space, 1 , 2 , 3 , 4 , 5 stand‐alone graphical simulation software, 6 , 7 , 8 , 9 , 10 using scripting application programming interface (API) to build integrated 3D collision detection software, 11 , 12 , 13 and using a priori patient and machine surface model to predict collision‐free space. 14 , 15 , 16 , 17 , 18 Both the vendor licensed and the home‐built collision detection methods have been widely investigated and adapted for photon LINAC‐based clinical workflow.…”

Section: Introductionmentioning

confidence: 99%

Design of a 3D patient‐specific collision avoidance virtual framework for half‐gantry proton therapy system

Dougherty

Whitaker

Mundy

et al. 2021

J Applied Clin Med Phys

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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 intensitymodulated 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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Collision prediction for intracranial stereotactic radiosurgery planning: An easy-to-implement analytical solution

Cited by 2 publications

References 18 publications

A quantitative framework for patient‐specific collision detection in proton therapy

A quantitative framework for patient‐specific collision detection in proton therapy

Design of a 3D patient‐specific collision avoidance virtual framework for half‐gantry proton therapy system

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