This study developed a model to automatically detect blurred areas in mammograms, which can affect diagnostic accuracy. Using a retrospective dataset consisting of 152 mammograms from three vendors, expert radiologists outlined blurred regions. Normalized Wiener spectra (nWS) were extracted and processed through a convolutional neural network (CNN) to classify images as either blurred or sharp. The model showed an AUROC of 0.808, with 78% agreement on blurred mammograms and 75% on sharp ones. The results suggest that frequency-based feature extraction can eliminate subjectivity in mammogram assessments, offering a more reliable tool for radiologists to improve diagnostic accuracy. Key points: Blurring in mammography limits radiologist interpretation and diagnostic accuracy. This objective blur detection tool ensures image quality, and reduces retakes and unnecessary exposures. Wiener spectrum analysis and CNN enabled automated blur detection in mammography. Article: Technical feasibility of automated blur detection in digital mammography using convolutional neural network Authors: S. Nowakowska, V. Vescoli, T. Schnitzler, C. Ruppert, K. Borkowski, A. Boss, C. Rossi, B. Wein & A. Ciritsis
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
