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

Serial and parallel organ-at-risk-specific noncoplanar arc optimization for small versus large target volumes in liver SBRT

Noncoplanar arc optimization has been shown to reduce OAR doses in SRS/SRT and has the potential to reduce doses to OARs in SBRT. Extracranial targets have additional considerations, including large OARs and, in the case of the liver, volume constraints on the healthy liver. Considering pathlengths through OARs that encompass target volumes may lead to specific dose reductions as in the encompassing healthy liver tissue. These optimizations must also leverage delivery efficiency and trajectory sampling to ensure ease of clinical translation. The purpose of this research is to generate optimized static-couch arcs that separately consider serial and parallel OARs and arc delivery efficiency, with a trajectory sampling metric, towards the aim of reducing dose to OARs and the surrounding healthy liver tissue. Separate BEV cost maps were created for parallel, and serial OARs by means of a fast ray-triangle intersection algorithm. An additional BEV cost map was created for the liver which, by definition, encompasses the liver tumors. The individual costs of these maps were summed and combined with the sampling metric for 100 000 random combinations of arc trajectories. A search algorithm was applied to find an arc trajectory solution that satisfied BEV cost and sampling optimization, while also ensuring an efficient delivery was possible with a low number of arcs. This method of arc selection was evaluated for 16 liver SBRT patients characterized by small and large target volumes. Comparisons were made with a clinical arc template of coplanar arcs. Dosimetric plan quality was evaluated using published guidelines and metrics from RTOG1112. Four of five plan quality metrics for the liver were significantly reduced when planned with optimized noncoplanar arcs. Median (range) reductions of the volumes receiving 10, 18, and 21 Gy were found of 140.4 (295.8) cc (p = 0.001), 28.2 (230.6) cc (p = 0.002) and 18.5 (155.5) cc (p = 0.04). A significant increase in median (range) dose to the right kidney of 0.2 ± 0.9 Gy (p = 0.03) was also found using optimized noncoplanar arcs, which was below the tolerance of 10 Gy for all cases. The average number of arcs chosen was 4 ± 1. Optimizing serial and parallel OARs separately during static couch noncoplanar arc selection significantly reduced the dose to the liver during SBRT using a moderate number of arcs.

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

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.

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.

Evaluation of plan quality and treatment efficiency in cranial stereotactic radiosurgery treatment plans with a variable source-to-axis distance

INTRODUCTION

Radiotherapy deliveries with dynamic couch motions that shorten the source-to-axis distance (SAD) on a C-arm linac have the potential to increase treatment efficiency through the increase of the effective dose rate. In this investigation, we convert clinically deliverable volumetric modulated arc therapy (VMAT) and dynamic conformal arc (DCA) plans for cranial radiosurgery into virtual isocenter plans through implementation of couch trajectories that maintain the target at a shortened but variable SAD throughout treatment.

MATERIALS AND METHODS

A randomly sampled population of patients treated with cranial radiosurgery from within the last three years were separated into groups with one, two, and three lesions. All plans had a single isocenter (regardless of number of targets), and a single prescription dose. Patient treatment plans were converted from their original delivery at a standard isocenter to a dynamic virtual isocenter in MATLAB. The virtual isocenter plan featured a variable isocenter position based upon the closest achievable source-to-target distance (referred to herein as a virtual source-to-axis distance [vSAD]) which avoided collision zones on a TrueBeam STx platform. Apertures were magnified according to the vSAD and monitor units at a given control point were scaled based on the inverse square law. Doses were calculated for the plans with a virtual isocenter in the Eclipse (v13.6.23) treatment planning system (TPS) and were compared with the clinical plans. Plan metrics (MU, Paddick conformity index, gradient index, and the volume receiving 12 Gy or more), normal brain dose-volume differences, as well as maximum doses received by organs at risk (OARs) were assessed. The values were compared between standard and virtual isocenter plans with Wilcoxon Sign Ranked tests to determine significance. A subset of the plans were mapped to the MAX-HD anthropomorphic phantom which contained an insert housing EBT3 GafChromic film and a PTW 31010 microion chamber for dose verification on a linac.

RESULTS

Delivering plans at a virtual isocenter resulted in an average reduction of 20.9% (p = 3×10^-6 ) and 20.6% (p = 3.0×10^-6 ) of MUs across all VMAT and all DCA plans, respectively. There was no significant change in OAR max doses received by plans delivered at a virtual isocenter. The low dose wash (1.0-2.0 Gy or 5-11% of the prescription dose) was increased (by approximately 20 cc) for plans with three lesions. This was equivalent to a 2.7%-3.8% volumetric increase in normal tissue receiving the respective dose level when comparing the plan with a virtual isocenter to a plan with a standard isocenter. Gamma pass rates with a 5%/1mm analysis criteria were 96.40% ± 2.90% and 95.07% ± 3.10% for deliveries at standard and virtual isocenter, respectively. Absolute point dose agreements were within -0.36% ± 3.45% and -0.55% ± 3.39% for deliveries at a standard and virtual isocenter, respectively. Potential time savings per arc were found to have linear relationship with the monitor units delivered per arc (savings of 0.009 s/MU with an r² = 0.866 when fit to plans with a single lesion).

CONCLUSIONS

Converting clinical plans at standard isocenter to a virtual isocenter design did not show any losses to plan quality while simultaneously improving treatment efficiency through MU reductions.

Region-of-interest intra-arc MV imaging to facilitate sub-mm positional accuracy with minimal imaging dose during treatment deliveries of small cranial lesions

Purpose

To automate the generation of region‐of‐interest (ROI) apertures for use with megavoltage imaging for online positional corrections during cranial stereotactic radiosurgery.

Materials and methods

Digitally reconstructed radiographs (DRRs) were created for a 3D‐printed skull phantom at 5 degree gantry angle increments for a three‐arc beam arrangement. At each angle, 3000 random rectangular apertures were generated, and 100 shifts on a grid were applied to the anatomy within the frame. For all shifts, the mutual information (MI) between the shifted and unshifted DRR was calculated to derive an average MI gradient. The top 10% of apertures that minimized registration errors were overlaid and discretely thresholded to generate imaging plans. Imaging was acquired with the skull while implementing simulated patient motion on a linac. Control point‐specific couch motions were derived to align the skull to its planned positioning.

Results

Apertures with a range of repositioning errors less than 0.1 mm possessed a 42% larger average MI gradient when compared with apertures with a range greater than 1 mm. Dose calculations with Monte Carlo exhibited an 84% reduction in the dose received by 50% of the skull with the 50% thresholded plan when compared to a constant 22 × 22 cm² imaging plan. For all different imaging plans (with and without motion), the calculated median 3D‐errors with respect to the tracking of a metal‐BB fiducial positioned at isocenter in the skull were sub‐mm except for the 80% thresholded plan.

Conclusions

Sub‐mm positional errors are achievable with couch motions derived from control point–specific ROI imaging. Smaller apertures that conform to an anatomical ROI can be utilized to minimize the imaging dose incurred at the expense of larger errors.