Evaluation of radiation detectors for the determination of field output factors in Leksell Gamma Knife dosimetry using 3D printed phantom inserts.

The Leksell Gamma Knife® Perfexion™ and Icon™ have a unique geometry, containing 192 60Co sources with collimation for field sizes of 4 mm, 8 mm, and 16 mm. 4 mm and 8 mm collimated fields lack lateral charged particle equilibrium, so accurate field output factors are essential. This study performs field output factor measurements for the microDiamond, microSilicon, and RAZOR™ Nano detectors. 3D printed inserts for the spherical Solid Water® Phantom were fabricated for microDiamond detector, the microSilicon unshielded diode and the RAZOR™ Nano micro-ionisation chamber. Detectors were moved iteratively to identify the peak detector signal for each collimator, representing the effective point of measurement of the chamber. In addition, field output correction factors were calculated for each detector relative to vendor supplied Monte Carlo simulated field output factors and field output factors measured with a W2 scintillator. All field output factors where within 1.1 % for the 4 mm collimator and within 2.3 % for the 8 mm collimator. The 3D printed phantom inserts were suitable for routine measurements if the user identifies the effective point of measurement, and ensures a reproducible setup by marking the rotational alignment of the cylindrical print. Measurements with the microDiamond and microSilicon can be performed faster compared to the RAZOR™ Nano due to differences in the signal to noise ratio. All detectors are suitable for field output factor measurements for the Leksell Gamma Knife® Perfexion™ and Icon™.

Investigation of Computed Tomography Numbers on Multiple Imaging Systems using Single and Multislice Methods.

Aim: The aim of this study is to determine the variation in Hounsfield values with single and multi-slice methods using in-house software on fan-beam computed tomography (FCT), linear accelerator (linac) cone-beam computed tomography (CBCT), and Icon-CBCT datasets acquired using Gammex and advanced electron density (AED) phantoms. Materials and Methods: The AED phantom was scanned on a Toshiba computed tomography (CT) scanner, five linac-based CBCT X-ray volumetric imaging systems, and Leksell Gamma Knife Icon. The variation between single and multi-slice methods was assessed by comparing scans acquired using Gammex and AED phantoms. The variation in Hounsfield units (HUs) between seven different clinical protocols was assessed using the AED phantom. A CIRS Model 605 Radiosurgery Head Phantom (TED) phantom was scanned on all three imaging systems to assess the target dosimetric changes due to HU variation. An in-house software was developed in MATLAB to assess the HU statistics and the trend along the longitudinal axis. Results: The FCT dataset showed a minimal variation (central slice ± 3 HU) in HU values along the long axis. A similar trend was also observed between the studied clinical protocols acquired on FCT. Variation among multiple linac CBCTs was insignificant. In the case of the water insert, a maximum HU variation of -7.23 ± 68.67 was observed for Linac 1 towards the inferior end of the phantom. All five linacs appeared to have a similar trend in terms of HU variation from the proximal to the distal end of the phantom, with a few outliers for Linac 5. Among three imaging modalities, the maximum variation was observed in gamma knife CBCTs, whereas FCT showed no appreciable deviation from the central value. In terms of dosimetric comparison, the mean dose in CT and Linac CBCT scans differed by <0.5 Gy, whereas at least a 1 Gy difference was observed between CT and gamma knife CBCT. Conclusion: This study shows a minimal variation with FCT between single, volume-based, and multislice methods, and hence the current approach of determining the CT-electron density curve based on a single-slice method would be sufficient for producing a HU calibrations curve for treatment planning. However, CBCTs acquired on linac, and in particular, gamma knife systems, show noticeable variations along the long axis, which is likely to affect the dose calculations performed on CBCTs. It is highly recommended to assess the Hounsfield values on multiple slices before using the HU curve for dose calculations.

Comparison and Validation of Multiple Detectors against Monte Carlo Simulation for the Use of Small-Field Dosimetry.

Aim: The aim of this study was to compare the Exradin W2 scintillator, PTW microDiamond, IBA Razor Nano, and IBA Razor chamber detectors for small-field dose measurements and validate the measured data against the EGSnrc user code and observe the variation between daisy-chained and direct measurement methods for the above detectors. Materials and Methods: The W2 scintillator, microDiamond, Razor Nano, and Razor chamber detectors were used to measure the in-plane and cross-plane profiles and the output factors (OFs) at 10 cm depth, and 90 source-to-surface distance for 6MV X-rays (Elekta Versa HD). The field sizes ranged from 0.5 cm × 0.5 cm to 5 cm × 5 cm. The BEAMnrc/DOSXYZnrc user codes (EGSnrc) were used to simulate the reference profiles. Gamma analysis was performed to compare the measured and simulated dose distributions. Results: The OFs measured with the W2 scintillator, microDiamond, Razor Nano chamber, Razor chamber, and the calculated Monte Carlo (MC) showed agreement to within 1% for the 3 cm × 3 cm field size. The uncertainty in the MC simulation was observed to be 0.4%. The percent difference in OFs measured using daisy-chained and direct measurement methods was within 0.15%, 0.4%, 1.4%, and 2.4% for microDiamond, W2 scintillator, Nano, and Razor chamber detectors, respectively. Conclusion: The lateral beam profiles and OFs of W2 scintillator, microDiamond, Razor Nano, and Razor chambers exhibit good agreement with the MC simulation within the nominal field sizes. Our results demonstrate that we can achieve considerable time-saving by directly measuring small-field OFs without daisy-chained methods using microDiamond and W2 scintillator. In terms of ease of use, sensitivity, reproducibility, and from a practical standpoint, we recommend microDiamond for small-field dosimetry.

A Framework for ExacTrac Dynamic Commissioning for Stereotactic Radiosurgery and Stereotactic Ablative Radiotherapy.

This paper aims to provide guidance and a framework for commissioning tests and tolerances for the ExacTrac Dynamic image-guided and surface-guided radiotherapy (SGRT) system. ExacTrac Dynamic includes a stereoscopic X-ray system, a structured light projector, stereoscopic cameras, thermal camera for SGRT, and has the capability to track breath holds and internal markers. The system provides fast and accurate image guidance and intrafraction guidance for stereotactic radiosurgery and stereotactic ablative radiotherapy. ExacTrac Dynamic was commissioned on a recently installed Elekta Versa HD. Commissioning tests are described including safety, isocenter calibration, dosimetry, image quality, data transfer, SGRT stability, SGRT localization, gating, fusion, implanted markers, breath hold, and end-to-end testing. Custom phantom designs have been implemented for assessment of the deep inspiration breath-hold workflow, the implanted markers workflow, and for gating tests where remote-controlled movement of a phantom is required. Commissioning tests were all found to be in tolerance, with maximum translational and rotational deviations in SGRT of 0.3 mm and 0.4°, respectively, and X-ray image fusion reproducibility standard deviation of 0.08 mm. Tolerances were based on published documents and upon the performance characteristics of the system as specified by the vendor. The unique configuration of ExacTrac Dynamic requires the end user to design commissioning tests that validate the system for use in the clinical implementation adopted in the department. As there are multiple customizable workflows available, tests should be designed around these workflows, and can be ongoing as workflows are progressively introduced into departmental procedures.

Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images.

PURPOSE: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). MATERIALS AND METHODS: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. RESULTS: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. CONCLUSION: The artifacts from the CBCT's limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.

Automatic Detection and Tracking of Marker Seeds Implanted in Prostate Cancer Patients using a Deep Learning Algorithm.

PURPOSE: Fiducial marker seeds are often used as a surrogate to identify and track the positioning of prostate volume in the treatment of prostate cancer. Tracking the movement of prostate seeds aids in minimizing the prescription dose spillage outside the target volume to reduce normal tissue complications. In this study, You Only Look Once (YOLO) v2™ (MathWorks™) convolutional neural network was employed to train ground truth datasets and develop a program in MATLAB that can visualize and detect the seeds on projection images obtained from kilovoltage (kV) X-ray volume imaging (XVI) panel (Elekta™). METHODS: As a proof of concept, a wax phantom containing three gold marker seeds was imaged, and kV XVI seed images were labeled and used as ground truth to train the model. The projection images were corrected for any panel shift using flex map data. Upon successful testing, labeled marker seeds and projection images of three patients were used to train a model to detect fiducial marker seeds. A software program was developed to display the projection images in real-time and predict the seeds using YOLO v2 and determine the centers of the marker seeds on each image. RESULTS: The fiducial marker seeds were successfully detected in 98% of images from all gantry angles; the variation in the position of the seed center was within ± 1 mm. The percentage difference between the ground truth and the detected seeds was within 3%. CONCLUSION: Our study shows that deep learning can be used to detect fiducial marker seeds in kV images in real time. This is an ongoing study, and work is underway to extend it to other sites for tracking moving structures with minimal effort.

A 3D printed phantom to assess MRI geometric distortion.

Geometric distortions in magnetic resonance can introduce significant uncertainties into applications such as radiotherapy treatment planning and need to be assessed as part of a comprehensive quality assurance program. We report the design, fabrication, and imaging of a custom 3D printed unibody MR distortion phantom along with quantitative image analysis.Methods: The internal cavity of the phantom is an orthogonal three-dimensional planar lattice, composed of 3 mm diameter rods spaced equidistantly at a 20 mm centre-centre offset repeating along the X, Y, and Z axes. The phantom featured an overall length of 308.5 mm, a width of 246 mm, and a height of 264 mm with lines on the external surface for phantom positioning matched to external lasers. The MR phantom was 3D printed in Nylon-12 using an advancement on traditional selective laser sintering (SLS) (HP Jet Fusion 3D-4200 machine). The phantom was scanned on a Toshiba Aquilion CT scanner to check the integrity of the 3D print and correct for any resultant issues. The phantom was then filled with NiSO4solution and scanned on a 3T PET-MR Siemens scanner for selected T1 and T2 sequences, from which distortion vectors were generated and analysed using in-house software written in Python.Results: All deviations of the node positions from the print design were less than 1 mm, with an average displacement of 0.228 mm. The majority of the deviations were smaller than the 0.692 mm pixel size for this dataset.Conclusion: A customised 3D printed MRI-phantom was successfully printed and tested for assessing geometric distortion on MRI scanners. 3D printed phantoms can be considered for clinics wishing to assess geometric distortions under specific conditions, but require resources for design, fabrication, commissioning, and verification.