Dynamic image reconstruction and synthesis framework using deep learning algorithm

Abstract

This research study shows an effective deformable complex 3D image reconstruction and image synthesis technique by consolidating needed high-level features from a deep convolutional neural network (CNN) system. By recognising the inherent deep features in image patches lead to information discovery in medicinal imaging. Utilising the ADNI and Loni imaging datasets, the performance of the proposed deep learning algorithm image reconstruction and synthesis performance was verified. For validation, various performance indices obtained with the proposed deep learning algorithm were compared with two conventional algorithms namely support vector machine and CNN. Likewise, to reveal the adaptability of the proposed image reconstruction and synthesis system, synthesis and reconstruction experiments were directed on the 7 T cerebrum magnetic resonance image. As presented in the study outcomes, the proposed method can accomplish predominant performance compared with other cutting-edge techniques with either low- or high-level features in terms of the synthesis and reconstruction rate. The proposed algorithm has a training time of 5 s with a structural similarity index of 0.97. In all investigations, the outcome shows that the proposed image reconstruction framework reliably exhibited progressively precise outcomes when contrasted with best in class. Hence, it can be used for possible precise image reconstruction and synthesis related applications.