Development and clinical implementation of eclipse scripting-based automated patient-specific collision avoidance software

Stereotactic Optimized Automated Radiotherapy (SOAR): a novel automated planning solution for multi-metastatic SRS compared to HyperArc™

Objective.Automated Stereotactic Radiosurgery (SRS) planning solutions improve clinical efficiency and reduce treatment plan variability. Available commercial solutions employ a template-based strategy that may not be optimal for all SRS patients. This study compares a novel beam angle optimized Volumetric Modulated Arc Therapy (VMAT) planning solution for multi-metastatic SRS to the commercial solution HyperArc.Approach.Stereotactic Optimized Automated Radiotherapy (SOAR) performs automated plan creation by combining collision prediction, beam angle optimization, and dose optimization to produce individualized high-quality SRS plans using Eclipse Scripting. In this retrospective study 50 patients were planned using SOAR and HyperArc. Assessed dose metrics included the Conformity Index (CI), Gradient Index (GI), and doses to organs-at-risk. Complexity metrics evaluated the modulation, gantry speed, and dose rate complexity. Plan dosimetric quality, and complexity were compared using double-sided Wilcoxon signed rank tests (α= 0.05) adjusted for multiple comparisons.Main Results.The median target CI was 0.82 with SOAR and 0.79 with HyperArc (p < .001). Median GI was 1.85 for SOAR and 1.68 for HyperArc (p < .001). The median V12Gy normal brain volume for SOAR and HyperArc were 7.76 cm3and 7.47 cm3respectively. Median doses to the eyes, lens, optic nerves, and optic chiasm were statistically significant favoring SOAR. The SOAR algorithm scored lower for all complexity metrics assessed.Significance.In-house developed automated planning solutions are a viable alternative to commercial solutions. SOAR designs high-quality patient-specific SRS plans with a greater degree of versatility than template-based methods.

A novel, end-to-end framework for avoiding collisions between the patient's body and gantry in proton therapy

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.

Technical note: A collision prediction tool using Blender

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.

Source-detector trajectory optimization in cone-beam computed tomography: a comprehensive review on today’s state-of-the-art

Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.

Deep learning tools for the cancer clinic: an open-source framework with head and neck contour validation

Background

With the rapid growth of deep learning research for medical applications comes the need for clinical personnel to be comfortable and familiar with these techniques. Taking a proven approach, we developed a straightforward open-source framework for producing automatic contours for head and neck planning computed tomography studies using a convolutional neural network (CNN).

Methods

Anonymized studies of 229 patients treated at our clinic for head and neck cancer from 2014 to 2018 were used to train and validate the network. We trained a separate CNN iteration for each of 11 common organs at risk, and then used data from 19 patients previously set aside as test cases for evaluation. We used a commercial atlas-based automatic contouring tool as a comparative benchmark on these test cases to ensure acceptable CNN performance. For the CNN contours and the atlas-based contours, performance was measured using three quantitative metrics and physician reviews using survey and quantifiable correction time for each contour.

Results

The CNN achieved statistically better scores than the atlas-based workflow on the quantitative metrics for 7 of the 11 organs at risk. In the physician review, the CNN contours were more likely to need minor corrections but less likely to need substantial corrections, and the cumulative correction time required was less than for the atlas-based contours for all but two test cases.

Conclusions

With this validation, we packaged the code framework and trained CNN parameters and a no-code, browser-based interface to facilitate reproducibility and expansion of the work. All scripts and files are available in a public GitHub repository and are ready for immediate use under the MIT license. Our work introduces a deep learning tool for automatic contouring that is easy for novice personnel to use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-01982-y.

Patient-Specific Collision Zones for 4π Trajectory Optimized Radiation Therapy

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.

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

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.

Automation and integration of a novel restricted single-isocenter stereotactic body radiotherapy (a-RESIST) method for synchronous two lung lesions

Synchronous treatment of two lung lesions using a single-isocenter volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) plan can decrease treatment time and reduce the impact of intrafraction motion. However, alignment of both lesions on a single cone beam CT (CBCT) can prove difficult and may lead to setup errors and unacceptable target coverage loss. A Restricted Single-Isocenter Stereotactic Body Radiotherapy (RESIST) method was created to minimize setup uncertainties and provide treatment delivery flexibility. RESIST utilizes a single-isocenter placed at patient's midline and allows both lesions to be planned separately but treated in the same session. Herein is described a process of automation of this novel RESIST method. Automation of RESIST significantly reduced treatment planning time while maintaining the benefits of RESIST. To demonstrate feasibility, ten patients with two lung lesions previously treated with a single-isocenter clinical VMAT plan were replanned manually with RESIST (m-RESIST) and with automated RESIST (a-RESIST). a-RESIST method automatically sets isocenter, creates beam geometry, chooses appropriate dose calculation algorithms, and performs VMAT optimization using an in-house trained knowledge-based planning model for lung SBRT. Both m-RESIST and a-RESIST showed lower dose to normal tissues compared to manually planned clinical VMAT although a-RESIST provided slightly inferior, but still clinically acceptable, dose conformity and gradient indices. However, a-RESIST significantly reduced the treatment planning time to less than 20 min and provided a higher dose to the lung tumors. The a-RESIST method provides guidance for inexperienced planners by standardizing beam geometry and plan optimization using DVH estimates. It produces clinically acceptable two lesions VMAT lung SBRT plans efficiently. We have further validated a-RESIST on phantom measurement and independent pretreatment dose verification of another four selected 2-lesions lung SBRT patients and implemented clinically. Further development of a-RESIST for more than two lung lesions and refining this approach for extracranial oligometastastic abdominal/pelvic SBRT, including development of automated simulated collision detection algorithm, merits future investigation.

CT-Based Collision Prediction Software for External-Beam Radiation Therapy

Purpose

Beam angle optimization is a critical issue for modern radiotherapy (RT) and is a challenging task, especially for large body sizes and noncoplanar designs. Noncoplanar RT techniques may have dosimetric advantages but increase the risk of mechanical collision. We propose a software solution to accurately predict colliding/noncolliding configurations for coplanar and noncoplanar beams.

Materials and Methods

Individualized software models for two different linear accelerators were built to simulate noncolliding gantry orientations for phantom/patient subjects. The sizes and shapes of the accelerators were delineated based on their manuals and on-site measurements. The external surfaces of the subjects were automatically contoured based on computed tomography (CT) simulations. An Alderson Radiation Therapy phantom was used to predict the accuracy of spatial collision prediction by the software. A gantry collision problem encountered by one patient during initial setup was also used to test the validity of the software. Results: In the comparison between the software estimates and on-site measurements, the noncoplanar collision angles were all predicted within a 5-degree difference in gantry position. The confusion matrix was calculated for each of the two empty accelerator models, and the accuracies were 98.7% and 97.3%. The true positive rates were 97.7% and 96.9%, while the true negative rates were 99.8% and 97.9%, respectively. For the phantom study, the collision angles were predicted within a 5-degree difference. The software successfully predicted the collision problem encountered by the breast cancer patient in the initial setup position and generated shifted coordinates that were validated to correspond to a noncolliding geometry.

Conclusion

The developed software effectively and accurately predicted collisions for accelerator-only, phantom, and patient setups. This software may help prevent collisions and expand the range of spatially applicable beam angles.

An open-source tool to visualize potential cone collisions while planning SRS cases

Purpose

To create an open‐source visualization program that allows one to find potential cone collisions while planning intracranial stereotactic radiosurgery cases.

Methods

Measurements of physical components in the treatment room (gantry, cone, table, localization stereotactic radiation surgery frame, etc.) were incorporated into a script in MATLAB (MathWorks, Natick, MA) that produces three‐dimensional visualizations of the components. A localization frame, used during simulation, fully contains the patient. This frame was used to represent a safety zone for collisions. Simple geometric objects are used to approximate the simulated components. The couch is represented as boxes, the gantry head and cone are represented by cylinders, and the patient safety zone can be represented by either a box or ellipsoid. These objects are translated and rotated based upon the beam geometry and the treatment isocenter to mimic treatment. A simple graphical user interface (GUI) was made in MATLAB (compatible with GNU Octave) to allow users to pass the treatment isocenter location, the initial and terminal gantry angles, the couch angle, and the number of angular points to visualize between the initial and terminal gantry angle.

Results

The GUI provides a fast and simple way to discover collisions in the treatment room before the treatment plan is completed. Twenty patient arcs were used as an end‐to‐end validation of the system. Seventeen of these appeared the same in the software as in the room. Three of the arcs appeared closer in the software than in the room. This is due to the treatment couch having rounded corners, whereas the software visualizes sharp corners.

Conclusions

This simple GUI can be used to find the best orientation of beams for each patient. By finding collisions before a plan is being simulated in the treatment room, a user can save time due to replanning of cases.

Prediction of conical collimator collision for stereotactic radiosurgery

The purpose of this study is to predict the collision clearance distance of stereotactic cones with treatment setup devices in cone-based stereotactic radiosurgery (SRS). The BrainLAB radiosurgery system with a Frameless Radiosurgery Positioning Array and dedicated couch top was targeted in this study. The positioning array and couch top were scanned with CT simulators, and their outer contours of were detected. The minimum clearance distance was estimated by calculating the Euclidian distances between the surface of the SRS cones and the nearest surface of the outer contours. The coordinate transformation of the outer contour was performed by incorporating the Beam's Eye View at a planned arc range and couch angle. From the minimum clearance distance, the collision-free gantry ranges for each couch angle were sequentially determined. An in-house software was developed to calculate the clearance distance between the cone surface and the outer contours, and thus determine the occurrence of a collision. The software was extensively tested for various combinations of couch and arc angles at multiple isocenter locations for two combinations of cone-couch systems. A total of 50 arcs were used to validate the calculation accuracies of the software for each system. The calculated minimum distances and collision-free angles from the software were verified by physical measurements. The calculated minimum distances were found to agree with the measurements to within 0.3 ± 0.9 mm. The collision-free arc angles from the software also agreed with the measurements to within 1.1 ± 1.1° with a 5-mm safety margin for 20 arcs. In conclusion, the in-house software was able to calculate the minimum clearance distance with <1.0 mm accuracy and to determine the collision-free arc range for the cone-based BrainLab SRS system.

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

PURPOSE

Gantry collision is a concern in linac-based stereotactic radiosurgery (SRS). Without collision screening, the planner may compromise optimal planning, unnecessary re-planning delays can occur, and incomplete treatments may be delivered. To address these concerns, we developed a software for collision prediction based on simple machine measurements.

MATERIALS AND METHODS

Three types of collision were identified; gantry-couch mount, gantry-couch and gantry-patient. Trigonometric formulas to calculate the distance from each potential point of collision to the gantry rotation axis were generated. For each point, collision occurs when that distance is greater than the gantry head to gantry rotational axis distance. The colliding arc for each point is calculated. A computer code incorporating these formulas was generated. The inputs required are the couch coordinates relative to the isocenter, the patient dimensions, and the presence or absence of a circular SRS collimator. The software outputs the collision-free gantry angles, and for each point, the shortest distance to the gantry or the colliding sector when collision is identified. The software was tested for accuracy on a TrueBEAM® machine equipped with BrainLab® accessories for 80 virtual isocenter-couch angle configurations with and without a circular collimator and a parallelepiped phantom.

RESULTS

The software predicted the absence of collision for 19 configurations. The mean absolute error between the measured and predicted gantry angle of collision for the remaining 61 cases was 0.86 (0.01-2.49).

CONCLUSION

This tool accurately predicted collisions for linac-based intracranial SRS and is easy to implement in any radiotherapy facility.

A practical method for predicting patient-specific collision in radiotherapy

Purpose

To develop a practical method for predicting patient‐specific collision during the treatment planning process.

Materials and method

Based on geometry information of the accelerator gantry and the location of plan isocenter, the collision‐free space region could be determined. In this study, collision‐free space region was simplified as a cylinder. Radius of cylinder was equal to the distance from isocenter to the collimator cover. The collision‐free space was converted and imported into treatment planning system (TPS) in the form of region of interest (ROI) which was named as ROISS. Collision was viewed and evaluated on the fusion images of patient's CT and ROIs in TPS. If any points of patient's body or couch fell beyond the safety space, collision would occur. This method was implemented in the Pinnacle TPS. The impact of safety margin on accuracy was also discussed. Sixty‐five plans of clinical patients were chosen for the clinical validation.

Results

When the angle of couch is zero, the ROISS displays as a series of circles on the cross section of the patient's CT. When the couch angle is not zero, ROISS is a series of ellipses in the transverse view of patient's CT. The ROISS can be generated quickly within five seconds after a single mouse click in TPS. Adding safety margin is an effective measure in preventing collisions from being undetected. Safety margin could increase negative predictive value (NPV) of test cases. Accuracy obtained was 96.3% with the 3 cm safety margin with 100% true positive collision detection.

Conclusion

This study provides a reliable, accurate, and fast collision prediction during the treatment planning process. Potential collisions can be discovered and prevented early before delivering. This method can integrate with the current clinical workflow without any additional required resources, and contribute to improvement in the safety and efficiency of the clinic.

Development and clinical implementation of eclipse scripting-based automated patient-specific collision avoidance software

PURPOSE

Increased use of Linac-based stereotactic radiosurgery (SRS), which requires highly noncoplanar gantry trajectories, necessitates the development of efficient and accurate methods of collision detection during the treatment planning process. This work outlines the development and clinical implementation of a patient-specific computed tomography (CT) contour-based solution that utilizes Eclipse Scripting to ensure maximum integration with clinical workflow.

METHODS

The collision detection application uses triangle mesh structures of the gantry and couch, in addition to the body contour of the patient taken during CT simulation, to virtually simulate patient treatments. Collision detection is performed using Binary Tree Hierarchy detection methods. Algorithm accuracy was first validated for simple cuboidal geometry using a calibration phantom and then extended to an anthropomorphic phantom simulation by comparing the measured minimum distance between structures to the predicted minimum distance for all allowable orientations. The collision space was tested at couch angles every 15° from 90 to 270 with the gantry incremented by 5° through the maximum trajectory. Receiver operating characteristic curve analysis was used to assess algorithm sensitivity and accuracy for predicting collision events. Following extensive validation, the application was implemented clinically for all SRS patients.

RESULTS

The application was able to predict minimum distances between structures to within 3 cm. A safety margin of 1.5 cm was sufficient to achieve 100% sensitivity for all test cases. Accuracy obtained was 94.2% with the 5 cm clinical safety margin with 100% true positive collision detection. A total of 88 noncoplanar SRS patients have been currently tested using the application with one collision detected and no undetected collisions occurring. The average time for collision testing per patient was 2 min 58 s.

CONCLUSIONS

A collision detection application utilizing patient CT contours was developed and successfully clinically implemented. This application allows collisions to be detected early during the planning process, avoiding patient delays and unnecessary resource utilization if detected during delivery.