IMRT QA result prediction via MLC transmission decomposition.

BACKGROUND: Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable. PURPOSE: We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics. METHODS: The tool was created in Python to read in DICOM plan files and determine the beam fluence fraction incident on each of seven different zones, each classified based on the RayStation MLC model. The fractions, termed grid point fractions, were validated by analyzing simple test plans. The average grid point fractions, over all control points for 46 plans were then computed. These values were then compared with gamma analysis pass percentages and median dose differences to determine if any significant correlations existed. RESULTS: Significant correlation was found between the grid point fraction metrics and median dose differences, but not with gamma analysis pass percentages. Correlations were positive or negative, suggesting differing model parameter value sensitivities, as well as potential insight into the treatment planning system dose model. CONCLUSIONS: By decomposing MLC control points into different transmission zones, it is possible to create a metric that predicts whether the analyzed plan will pass a quality assurance measurement from a dose calculation accuracy standpoint. The tool and metrics developed in this work have potential applications in comparing clinical beam models or identifying their weak points. Implementing the tool within a treatment planning system would also provide more potential plan optimization parameters.

The Effect of Mouse Size on Dose from an X-Rad320 Irradiator.

Irradiation protocols for murine experiments often use standardized dose rate estimates for calculating dose delivered, regardless of physical variations between mouse subjects. This work sought to determine the significance of mouse size on absorbed dose. Five mouse-like phantoms of various sizes based on the mouse whole-body (MOBY) model were 3D printed. The phantoms were placed in an X-Rad320 biological irradiator and a standard irradiation protocol was used to deliver dose. Dose was measured using thermoluminescent dosimeter (TLD) microcubes inside each phantom, and the relative readings were used to calculate output factors (OFs), normalized to the phantom of median volume. Additionally, the OF for each mouse was simulated in Monte Carlo N-Particle (MCNP) code. For both the TLD measurements and MCNP simulations, the OF for each mouse was determined by both experiments and calculations to be unity within the relative standard uncertainties (k = 1). This work supports comparing results across various studies using the X-Rad320 irradiator without need for corrections based on mouse size.

Mental models, visual reasoning and interaction in information visualization: a top-down perspective.

Although previous research has suggested that examining the interplay between internal and external representations can benefit our understanding of the role of information visualization (InfoVis) in human cognitive activities, there has been little work detailing the nature of internal representations, the relationship between internal and external representations and how interaction is related to these representations. In this paper, we identify and illustrate a specific kind of internal representation, mental models, and outline the high-level relationships between mental models and external visualizations. We present a top-down perspective of reasoning as model construction and simulation, and discuss the role of visualization in model based reasoning. From this perspective, interaction can be understood as active modeling for three primary purposes: external anchoring, information foraging, and cognitive offloading. Finally we discuss the implications of our approach for design, evaluation and theory development.

Knowledge precepts for design and evaluation of information visualizations.

The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a framework for the design and evaluation of information visualization systems, and demonstrate its use.