LAUSR

OBJECTIVE Quality assurance (QA) testing must be performed at regular intervals to ensure that medical devices are operating within designed specifications. Numerous QA phantoms and software packages have been developed to facilitate measurements of machine performance. However, due to the hard-coded nature of geometric phantom definition in analysis software, users are typically limited to the use of a small subset of compatible QA phantoms. In this work, we present a novel AI-based Universal Phantom (UniPhan) algorithm that is not phantom specific and can be easily adapted to any pre-existing image-based QA phantom. Approach: Extensible Markup Language Scalable Vector Graphics (XML-SVG) was modified to include several new tags describing the function of embedded phantom objects for use in QA analysis. Functional tags include contrast and density plugs, spatial linearity markers, resolution bars and edges, uniformity regions, and light-radiation field coincidence areas. Machine learning was used to develop an image classification model for automatic phantom type detection. After AI phantom identification, UniPhan imported the corresponding XML-SVG wireframe, register it to the image taken during the QA process, perform analysis on the functional tags, and export results for comparison to expected device specifications. Analysis results were compared to those generated by manual image analysis. Main Results: XML-SVG wireframes were generated for several commercial phantoms including ones specific to CT, CBCT, kV planar imaging, and MV imaging. Several functional objects were developed and assigned to the graphical elements of the phantoms. The AI classification model was tested for training and validation accuracy and loss, along with phantom type prediction accuracy and speed. The results reported training and validation accuracies of 99%, phantom type prediction confidence scores of around 100%, and prediction speeds of around 0.1 seconds. Compared to manual image analysis, Uniphan results were consistent across all metrics including contrast-to-noise ratio (CNR), modulation transfer function (MTF), HU accuracy, and uniformity. Significance: The UniPhan method can identify phantom type and use its corresponding wireframe to perform QA analysis. As these wireframes can be generated in a variety of ways this represents an accessible automated method of analyzing image quality phantoms that is flexible in scope and implementation.