Automatic CT Quality Assurance Artifact Detection Using Residual-Enhanced Teacher–Student Learning Artificial Intelligence
Routine CT quality assurance (QA) relies on visual inspection, where subtle non-uniformities can be difficult to identify consistently across scanners and readers. We developed an efficient, interpretable artificial intelligence-assisted tool that detects and...
Poster Program · Diagnostic and Interventional Radiology Physics