Publications by authors named "C K Abbey"
Background Studies suggest that readers experience perceptual adaptation when interpreting batched screening mammograms, which may serve as a mechanism for improved performance. Purpose To analyze clinical digital breast tomosynthesis (DBT) screening data to evaluate changes in reader performance during sequential batch reading. Materials and Methods This observational retrospective study used data from the radiology information system collected for screening DBT examinations performed from January 2018 to December 2019.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to see if the order in which mammograms are read, based on their characteristics, could help radiologists spot abnormalities more efficiently, particularly focusing on factors like breast density and visual adaptation.
- A total of 150 mammograms were analyzed by 13 radiologists using three reading methods: random order, increasing volumetric breast density (VBD), and self-supervised learning (SSL) grouping.
- Results indicated that readings organized by increasing VBD led to slightly better performance and reduced reading time compared to random order, without affecting sensitivity and specificity significantly.
Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans.
View Article and Find Full Text PDFPurpose: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance.
Methods: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients.