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.

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.

The influence of the iridium oxide thin film on the electrocatalytic properties of platinum nanoparticles was investigated using the electro-oxidation of methanol and CO as a probe. The presence of the IrO(2) thin film leads to the homogeneous dispersion of Pt nanoparticles. For comparison, polycrystalline platinum and Pt nanoparticles dispersed on a Ti substrate in the absence of an IrO(2) layer (Ti/Pt) were also investigated in this study. Inverted and enhanced CO bipolar peaks were observed using an in situ electrochemical Fourier transform infrared technique during the methanol oxidation on the Pt nanoparticles dispersed on a Ti substrate. Electrochemical impedance studies showed that the charge transfer resistance was significantly lower for the Ti/IrO(2)/Pt electrode compared with that of the massive Pt and Ti/Pt nanoparticles. The presence of the IrO(2) thin film not only greatly increases the active surface area but also promotes CO oxidation at a much lower electrode potential, thus, significantly enhancing the electrocatalytic activity of Pt nanoparticles toward methanol electro-oxidation.

For ion chambers with cavities open to the surrounding atmosphere, the response measured at a given temperature and pressure must be corrected using the standard temperature-pressure correction factor (P(TP)). A previous paper based solely on Monte Carlo simulations [D. J. La Russa and D. W. O. Rogers, Med. Phys. 33, 4590-4599 (2006)] pointed out the shortcomings of the P(TP) correction factor when used to correct the response of non-air-equivalent chambers for low-energy x-ray beams. This work presents the results of several experiments that corroborate these calculations for a number of ion chambers. Monte Carlo simulations of the experimental setup revealed additional insight into the various factors affecting the extent of the breakdown of P(TP), including the effect of impurities and the sensitivity to chamber dimensions. For an unfiltered 60 kV beam, the P(TP)-corrected response of an NE 2571 ion chamber measured at 0.7 atm was 2.5% below the response measured at reference conditions. In general, Monte Carlo simulations of the experimental setup using EGSnrc were within 0.5% of measured values. EGSnrc-calculated values of air kerma calibration coefficients (N(K)) at low x-ray energies are also provided as a means of estimating the level of impurities in the chambers investigated. Calculated values of N(K) normalized to the value measured for a 250 kV beam were obtained for three chambers and were within 1% of experiment with one exception, the Exradin A12 in a 50 kV beam.