A Pilot Clinical and Technical Validation of an Immersive Virtual Reality Platform for 3D Anatomical Modeling and Contouring in Support of Surgical and Radiation Oncology Treatment Planning.

The aim of this study was to validate a novel medical virtual reality (VR) platform used for medical image segmentation and contouring in radiation oncology and 3D anatomical modeling and simulation for planning medical interventions, including surgery. The first step of the validation was to verify quantitatively and qualitatively that the VR platform can produce substantially equivalent 3D anatomical models, image contours, and measurements to those generated with existing commercial platforms. To achieve this, a total of eight image sets and 18 structures were segmented using both VR and reference commercial platforms. The image sets were chosen to cover a broad range of scanner manufacturers, modalities, and voxel dimensions. The second step consisted of evaluating whether the VR platform could provide efficiency improvements for target delineation in radiation oncology planning. To assess this, the image sets for five pediatric patients with resected standard-risk medulloblastoma were used to contour target volumes in support of treatment planning of craniospinal irradiation, requiring complete inclusion of the entire cerebral-spinal volume. Structures generated in the VR and the commercial platforms were found to have a high degree of similarity, with dice similarity coefficient ranging from 0.963 to 0.985 for high-resolution images and 0.920 to 0.990 for lower resolution images. Volume, cross-sectional area, and length measurements were also found to be in agreement with reference values derived from a commercial system, with length measurements having a maximum difference of 0.22 mm, angle measurements having a maximum difference of 0.04°, and cross-sectional area measurements having a maximum difference of 0.16 mm2. The VR platform was also found to yield significant efficiency improvements, reducing the time required to delineate complex cranial and spinal target volumes by an average of 50% or 29 min.

Dosimetric considerations for moldable silicone composites used in radiotherapy applications.

Due to their many favorable characteristics, moldable silicone (MS) composites have gained popularity in medicine and recently, in radiotherapy applications. We investigate the dosimetric properties of silicones in radiotherapy beams and determine their suitability as water substitutes for constructing boluses and phantoms. Two types of silicones were assessed ( ρ $\rho \;$ = 1.04 g/cm3 and ρ $\rho \;$ = 1.07 g/cm3 ). Various dosimetric properties were characterized, including the relative electron density, the relative mean mass energy-absorption coefficient, and the relative mean mass restricted stopping power. Silicone slabs with thickness of 1.5 cm and 5.0 cm were molded to mimic a bolus setup and a phantom setup, respectively. Measurements were conducted for Co-60 and 6 MV photon beams, and 6 MeV electron beams. The doses at 1.5 cm and 5.0 cm depths in MS were measured with solid water (SW) backscatter material (DMS-SW ), and with a full MS setup (DMS-MS ), then compared with doses at the same depths in a full SW setup (DSW-SW ). Relative doses were reported as DMS-SW /DMS-SW and DMS-MS /DSW-SW . Experimental results were verified using Monaco treatment planning system dose calculations and Monte Carlo EGSnrc simulations. Film measurements showed varying dose ratios according to MS and beam types. For photon beams, the bolus setup DMS-SW /DSW-SW exhibited a 5% relative dose reduction. The dose for 6 MV beams was reduced by nearly 2% in a full MS setup. Up to 2% dose increase in both scenarios was observed for electron beams. Compared with dose in SW, an interface of MS-SW can cause relatively high differences. We conclude that it is important to characterize a particular silicone's properties in a given beam quality prior to clinical use. Because silicone compositions vary between manufacturers and differ from water/SW, accurate dosimetry using these materials requires consideration of the reported differences.

Can we rely on surgical clips placed during oncoplastic breast surgery to accurately delineate the tumor bed for targeted breast radiotherapy?

PURPOSE: Oncoplastic breast surgery (OBS) is gaining popularity among surgeons for breast-conserving surgery treatments. OBS relies on complex relocation and deformation of breast tissue involving the tumor bed (TB). In this study, we investigate the validity of using surgical clips with OBS for accurate TB delineation in adjuvant, targeted breast radiotherapy. METHODS: Different OBS techniques were simulated on realistic breast phantoms. Surgical clips were used to demarcate the TB. Following tumor resection and closure, the true TB (TBTrue) was extracted. Each phantom was CT imaged at several phases of surgery in order to record pre- and post-OBS closure surgical clip displacements. Two senior radiation oncologists (ROs) were asked to delineate TBs on CTs by relying on surgical clips placed as per standard protocol, and by referring to operative notes. Their original contours, as well as those expanded using 5-15 mm margins, were compared with the accurate TBTrue using the dice similarity coefficient (DSC), Hausdorff Distance (HD), and over- and under-contoured volumes. Inter- and intra-RO contour agreements were also evaluated. RESULTS: Post-OBS surgical clips were significantly displaced outside the original breast quadrant. Inter- and Intra-RO TB contours were consistent, yet systematically differed from TBTrue (DSC values range = 0.38 to 0.69, and maximum HD range = 17.8 mm to 38.0 mm). Using expansion margins did not improve contour congruence and caused significant over-contoured volumes. CONCLUSION: Following OBS, surgical clips alone are not reliable radiographic surrogates of TB locations and accurate TB delineation is challenging. For complex OBS cases, indication of any type of partial breast irradiation is very questionable.

Dose-sparing effect of deep inspiration breath hold technique on coronary artery and left ventricle segments in treatment of breast cancer.

BACKGROUND AND PURPOSE: The risk of radiation-induced cardiac injury remains a challenging problem in the treatment of breast cancer. Certain cardiac structures receive higher doses than others, which results in variable frequencies of radiation-induced injuries across these structures. Radiation dose can be reduced using the deep inspiration breath hold (DIBH) technique. We aimed to investigate the dose reductions from DIBH in individual cardiac segments. MATERIALS AND METHODS: A dosimetric analysis was performed on left-sided breast cancer patients who underwent breast-conserving surgery and whole breast irradiation. Radiation doses to the cardiac structures were compared between the DIBH and free-breathing (FB) techniques and the dose reductions with DIBH were correlated to the lung expansion. RESULTS: For the 75 patients included in our study, DIBH effectively reduced doses to the heart, left lung, left anterior descending coronary artery (LAD) and left ventricle (LV), but the degree of dose reductions was variable across different structures. The absolute dose reductions were greatest in the distal LAD (14.4 Gy) and apical LV (12.1 Gy) segments, compared with the other LAD (middle 9.7 Gy, proximal 1.6 Gy) and LV (anterior 5.3 Gy, lateral 2.9 Gy, septal 2.0 Gy, inferior 0.2 Gy) segments. Left lung expansion was significantly correlated with the dose reductions in the LAD (Spearman's rank correlation coefficient, ρ, 0.304) and LV (ρ, 0.420) segments. CONCLUSIONS: Our study demonstrates the dose-sparing effects of DIBH in various cardiac structures, especially the distal LAD and apical LV segments. The large dose reductions seen in the distal LAD and apical LV segments could potentially translate into clinical benefit of reduced cardiac toxicity, as these structures have been previously shown to receive the highest doses and are associated with radiation-induced injury.

Radiological, dosimetric and mechanical properties of a deformable breast phantom for radiation therapy and surgical applications.

The displacement of tumor bed walls during oncoplastic breast surgery (OBS) decreases the accuracy of using surgical clips as the sole surrogate for tumor bed location. This highlights the need for better communication of OBS techniques to radiation oncologists. To facilitate OBS practice and investigate clip placement reliability, a realistic silicone-based breast phantom was constructed with components emulating a breast parenchyma, epidermis, areola, nipple, chest wall, and a tumor. OBS was performed on the phantom and surgical clips were placed to mark the tumor bed. The phantom was imaged with CT, MRI, and ultrasound (US). The parenchyma's signal-to-noise ratio (SNR) and clips to parenchyma's contrast-to-noise ratio (CNR) were measured. The phantom's CT Hounsfield Unit (HU), relative electron density (RED), and mass density were determined. 6 and 10 MV photon beam attenuation measurements were performed in phantom material. The Young's Modulus and ultimate tensile strength (UTS) of the phantom parenchyma and epidermis were measured. Results showed that the breast phantom components were visible on all imaging modalities with adequate SNR and CNR. The phantom's HU is 130 ± 10. The RED is 0.983. Its mass density is 1.01 ± 0.01 g cm-3. Photon attenuation measurements in phantom material were within 1% of those in water. The Young's Moduli were 13.4 ± 4.2 kPa (mechanical) and 30.2 ± 4.1 kPa (US elastography) for the phantom parenchyma. The UTS' were 0.05 ± 0.01 MPa (parenchyma) and 0.23 ± 0.12 MPa (epidermis). We conclude that the phantom's imaging characteristics resemble a fibroglandular breast's and allow clear visualization of high-density markers used in radiation therapy. The phantom material is suitable for dose measurements in MV photon beams. Mechanical results confirmed the phantom's similarity to breast tissue. The phantom enables investigation of surgical clip displacements pre- and post-OBS, and is useful for radiation therapy quality assurance applications.

Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.

The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assessment of delivery quality. The goal of this work is to investigate the potential of using treatment plan characteristics and linac performance metrics (i.e. quality control test results) in combination with machine learning techniques to predict the results of VMAT patient-specific QA measurements. Using features that describe treatment plan complexity and linac performance metrics, we trained a linear support vector classifier (SVC) to classify the results of VMAT patient-specific QA measurements. The 'targets' in this model were simple classes representing median dose difference between measured and expected dose distributions-'hot' if the median dose deviation was >1%, 'cold' if it was <-1%, and 'normal' if it was within ±1%. A total of 1620 unique patient-specific QA measurements were available for model development and testing. 75% of the data were used to develop and cross-validate the model, and the remaining 25% were used for an independent assessment of model performance. For the model development phase, a recursive feature elimination (RFE) cross-validation technique was used to eliminate unimportant features. Model performance was assessed using receiver operator characteristic (ROC) curve metrics. Of the ten features found to be most predictive of patient-specific QA measurement results, half were derived from treatment plan characteristics and half from quality control (QC) metrics characterizing linac performance. The model achieved a micro-averaged area under the ROC curve of 0.93, and a macro-averaged area under the ROC curve of 0.88. This work demonstrates the potential of using both treatment plan characteristics and routine linac QC results in the development of machine learning models for VMAT patient-specific QA measurements.

Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models.

Recent technological innovations have created new opportunities for the increased adoption of virtual reality (VR) and augmented reality (AR) applications in medicine. While medical applications of VR have historically seen greater adoption from patient-as-user applications, the new era of VR/AR technology has created the conditions for wider adoption of clinician-as-user applications. Historically, adoption to clinical use has been limited in part by the ability of the technology to achieve a sufficient quality of experience. This article reviews the definitions of virtual and augmented reality and briefly covers the history of their development. Currently available options for consumer-level virtual and augmented reality systems are presented, along with a discussion of technical considerations for their adoption in the clinical environment. Finally, a brief review of the literature of medical VR/AR applications is presented prior to introducing a comprehensive conceptual framework for the viewing and manipulation of medical images in virtual and augmented reality. Using this framework, we outline considerations for placing these methods directly into a radiology-based workflow and show how it can be applied to a variety of clinical scenarios.

Monte Carlo and analytic modeling of an Elekta Infinity linac with Agility MLC: Investigating the significance of accurate model parameters for small radiation fields.

PURPOSE: To explain the deviation observed between measured and Monaco calculated dose profiles for a small field (i.e., alternating open-closed MLC pattern). A Monte Carlo (MC) model of an Elekta Infinity linac with Agility MLC was created and validated against measurements. In addition, an analytic model which predicts the fluence at the isocenter plane was used to study the impact of multiple beam parameters on the accuracy of dose calculations for small fields. METHODS: A detailed MC model of a 6 MV Elekta Infinity linac with Agility MLC was created in EGSnrc/BEAMnrc and validated against measurements. An analytic model using primary and secondary virtual photon sources was created and benchmarked against the MC simulations and the impact of multiple beam parameters on the accuracy of the model for a small field was investigated. Both models were used to explain discrepancies observed between measured/EGSnrc simulated and Monaco calculated dose profiles for alternating open-closed MLC leaves. RESULTS: MC-simulated dose profiles (PDDs, cross- and in-line profiles, etc.) were found to be in very good agreements with measurements. The best fit for the leaf bank rotation was found to be 9 mrad to model the defocusing of Agility MLC. Moreover, a very good agreement was observed between results from the analytic model and MC simulations for a small field. Modifying the radial size of the incident electron beam in the BEAMnrc model improved the agreement between Monaco and EGSnrc calculated dose profiles by approximately 16% and 30% in the position of maxima and minima, respectively. CONCLUSION: Accurate modeling of the full-width-half-maximum (FWHM) of the primary photon source as well as the MLC leaf design (leaf bank rotation, etc.) is essential for accurate calculations of dose delivered by small radiation fields when using virtual source or MC models of the beam.

Experimental verification of 4D Monte Carlo simulations of dose delivery to a moving anatomy.

PURPOSE: To evaluate a novel 4D Monte Carlo simulation tool by comparing calculations to physical measurements using a respiratory motion phantom. METHODS: We used a dynamic Quasar phantom in both stationary and breathing states (sinusoidal motion of amplitude of 1.8 cm and period of 3.3 s) for dose measurements on an Elekta Agility linear accelerator. Gafchromic EBT3 film and the RADPOS 4D dosimetry system were placed inside the lung insert of the phantom to measure dose profiles and point-dose values at the center of the spherical tumor inside the insert. Both a static 4 × 4 cm2 field and a VMAT plan were delivered. Static and 4D Monte Carlo simulations of the treatment deliveries were performed using DOSXYZnrc and a modified version of the defDOSXYZnrc user code that allows modeling of the continuous motion of both machine and patient. DICOM treatment plan files and linac delivery log files were used to generate corresponding input files. The phantom motion recorded by RADPOS during beam delivery was incorporated into the input files for the 4DdefDOSXYZnrc simulations. RESULTS: For stationary phantom simulations, all point-dose values from MC simulations at the tumor center agreed within 1% with film and within 2% with RADPOS. More than 98% of the voxels from simulated dose profiles passed a 1D gamma of 2%/2-mm criteria against measured dose profiles. Similar results were observed when applying a 2D gamma analysis with a 2%/2-mm criteria to compare 2D dose distributions of Monte Carlo simulations against measurements. For simulations on the moving phantom, MC-calculated dose values at the center of the tumor were found to be within 1% of film and within 2σ of experimental uncertainties which are 2.8% of the RADPOS measurements. 1D gamma comparisons of the dose profiles were better than 91%, and 2D gamma comparisons of the 2D dose distributions were found to be better than 94%. CONCLUSION: Our 4D Monte Carlo method using defDOSXYZnrc can be used to accurately calculate the dose distribution in continuously moving anatomy for various treatment techniques. This work, if extended to deformable anatomies, can be used to reconstruct patient delivered dose for use in adaptive radiation therapy.

Position-probability-sampled Monte Carlo calculation of VMAT, 3DCRT, step-shoot IMRT, and helical tomotherapy dose distributions using BEAMnrc/DOSXYZnrc.

PURPOSE: The commercial release of volumetric modulated arc therapy techniques using a conventional linear accelerator and the growing number of helical tomotherapy users have triggered renewed interest in dose verification methods, and also in tools for exploring the impact of machine tolerance and patient motion on dose distributions without the need to approximate time-varying parameters such as gantry position, MLC leaf motion, or patient motion. To this end we have developed a Monte Carlo-based calculation method capable of simulating a wide variety of treatment techniques without the need to resort to discretization approximations. METHODS: The ability to perform complete position-probability-sampled Monte Carlo dose calculations was implemented in the BEAMnrc/DOSXZYnrc user codes of EGSnrc. The method includes full accelerator head simulations of our tomotherapy and Elekta linacs, and a realistic representation of continous motion via the sampling of a time variable. The functionality of this algorithm was tested via comparisons with both measurements and treatment planning dose distributions for four types of treatment techniques: 3D conformal, step-shoot intensity modulated radiation therapy, helical tomotherapy, and volumetric modulated are therapy. RESULTS: For static fields, the absolute dose agreement between the EGSnrc Monte Carlo calculations and measurements is within 2%/1 mm. Absolute dose agreement between Monte Carlo calculations and treatment planning system for the four different treatment techniques is within 3%/3 mm. Discrepancies with the tomotherapy TPS on the order of 10%/5 mm were observed for the extreme example of a small target located 15 cm off-axis and planned with a low modulation factor. The increase in simulation time associated with using position-probability sampling, as opposed to the discretization approach, was less than 2% in most cases. CONCLUSIONS: A single Monte Carlo simulation method can be used to calculate patient dose distribution for various types of treatment techniques delivered with either tomotherapy or a conventional linac. The method simplifies the simulation process, improves dose calculation accuracy, and involves an acceptably small change in computation time.

Development of a Monte Carlo model for the Brainlab microMLC.

Stereotactic radiosurgery with several static conformal beams shaped by a micro multileaf collimator (microMLC) is used to treat small irregularly shaped brain lesions. Our goal is to perform Monte Carlo calculations of dose distributions for certain treatment plans as a verification tool. A dedicated microMLC component module for the BEAMnrc code was developed as part of this project and was incorporated in a model of the Varian CL2300 linear accelerator 6 MV photon beam. As an initial validation of the code, the leaf geometry was visualized by tracing particles through the component module and recording their position each time a leaf boundary was crossed. The leaf dimensions were measured and the leaf material density and interleaf air gap were chosen to match the simulated leaf leakage profiles with film measurements in a solid water phantom. A comparison between Monte Carlo calculations and measurements (diode, radiographic film) was performed for square and irregularly shaped fields incident on flat and homogeneous water phantoms. Results show that Monte Carlo calculations agree with measured dose distributions to within 2% and/or 1 mm except for field size smaller than 1.2 cm diameter where agreement is within 5% due to uncertainties in measured output factors.