Brom, Kevin M.

Objectives Uniformity artifacts caused by defective transducer elements or scanner malfunctions degrade diagnostic image quality. Traditional quality control (QC) methods, such as phantom testing and visual or quantitative image analysis, can be labor‐intensive and limited in test frequency. This study aims to develop a deep learning framework to detect uniformity artifacts and complement traditional QC. Methods Clinical median images were generated by aggregating co‐registered grayscale ultrasound images acquired by each transducer and computing median intensity values across the image stack. A pretrained ResNet‐18 model was fine‐tuned on a dataset consisting of clinical median images from linear and curvilinear transducers. The dataset was divided into training, validation, and testing subsets, ensuring no overlap between training and test transducers. To assess generalizability, the model was also evaluated on an independent test set of 396 phantom‐validated images from linear and curvilinear transducers. Results The model achieved 100% accuracy on the first dataset's test set. On the independent test set, it attained 87.4% accuracy with high sensitivity (0.84) and specificity (0.88), demonstrating robust generalization. Occlusion sensitivity maps confirmed the model's attention to uniformity artifact regions. Conclusion The deep learning framework using clinical median images demonstrated robust performance across several linear and curvilinear transducer models. It could be integrated into the clinical QC workflow by automating artifact detection in an effective and timely manner. In our practice, it can flag median images classified as artifact‐present, reducing human review time by approximately 80% while preserving detection accuracy.

AbstractPurposeTo investigate the dependency of dose‐volume histogram (DVH) behavior and precision on underlying discretization using shapes and dose distributions with known analytical DVHs for five commercial DVH calculators.MethodsDVHs and summary metrics were extracted from all five systems using synthetic cone and cylinder objects for which the true volume and DVH curves were known. Trends in the curves and metrics were explored by varying the underlying voxelization of the CT image, structure set, and dose grid as well by varying the geometry of the structure and direction of a linear dose gradient. Using synthetic structures allowed for comparison with ground truth DVH curves to assess their accuracy while an algorithm was additionally developed to assess the precision of each system. The precision was calculated with a novel algorithm that treats any “stair step” behavior in a DVH curve as an uncertainty band and calculates the width, characterized as a percent difference, of the band for various DVH metrics. The underlying voxelization was additionally changed and DVHs were extracted for two clinical examples. The details of how each system calculated DVHs were also investigated and tendencies in the calculated curves, metrics, and precision were related to choices made in the calculation methodology.ResultsCalculation methodology differences that had a noticeable impact on the DVH curves and summary metrics include supersampling beyond the input grids and interpretation of the superior and inferior ends of the structures. Among the systems studied, the median precision ranged from 0.902% to 3.22%, and interquartile ranges varied from 1.09% to 3.91%.ConclusionsCommercial dose‐evaluation solutions can calculate different DVH curves, structure volume measures, and dose statistics for the same input data due to differences in their calculation methodologies. This study highlights the importance of understanding and investigating the DVH calculation when considering a new clinical system and when using more than one system for data transfer.