Skip to main content
Fig. 1 | BMC Medical Imaging

Fig. 1

From: Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience

Fig. 1

Training and reconstruction processing flow of super-resolution deep learning reconstruction. In training process (a), the DCNN is trained by a lot of target and input pairs. The HR and LN data which are acquired on UHRCT scanner is used as target. The LR and HN data which is simulated from target is used as input. In reconstruction process (b), the LR and HN data is reconstructed using the trained DCNN. Then the HR and LN data is obtained. DCNN, deep convolutional neural network; HR, high resolution; LN, low noise; UHRCT, ultrahigh spatial resolution computed tomography; LR, low resolution; HN, high noise

Back to article page

BMC Medical Imaging

ISSN: 1471-2342

Contact us