Deep learning reconstruction (DLR) is a novel method of reconstruction that introduces deep convolutional neural networks into the reconstruction flow. The authors of this research examined the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR and to compare it to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). Radiologists analysed and graded results from 46 patients in an attempt to compare the difference between CT images subjected to hybrid-IR, MBIR, and DLR. Key points The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction. Article: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT Authors: Motonori Akagi, Yuko Nakamura, Toru Higaki, Keigo Narita, Yukiko Honda, Jian Zhou, Zhou Yu, Naruomi Akino, Kazuo Awai
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
