Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in “double low” chest CTA in children: a feasibility study

• Published in: Radiologia medica Vol. 126; no. 9; pp. 1181 - 1188
• Main Authors: Sun, Jihang, Li, Haoyan, Gao, Jun, Li, Jianying, Li, Michelle, Zhou, Zuofu, Peng, Yun
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01.09.2021; Springer Nature B.V
• Subjects: Acceptable noise levels; Algorithms; Angiography; Aorta; Chest; Chest Radiology; Computed tomography; Coronary vessels; Deep learning; Diagnostic Radiology; Electric potential; Feasibility studies; Image contrast; Image enhancement; Image quality; Image reconstruction; Imaging; Interventional Radiology; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Muscles; Neuroradiology; Noise; Performance evaluation; Radiation; Radiation dosage; Radiology; Ultrasound; Voltage; Deep learning; Tomography; Thorax; Child; X-ray computed; Image reconstruction

Background Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventi...