Hybrid feature fusion neural network integrating transformer for DCE-MRI super resolution

Abstract

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is an essential medical imaging technique that provides multiple time-series images of the same anatomical structures and function without causing ionizing radiation. However, due to the physical nature of the DCE-MRI process, the scan time can be as long as tens of minutes, which severely affects the patient experience. Therefore, reducing scanning time has become a hot research topic, which super-resolution reconstruction from post-processing of the resulting low-resolution images to obtain high-resolution images. However, some existing methods exhibit general limitations in capturing long-range dependencies and loss of localized details. In this paper, we propose a Hybrid Feature Fusion Neural Network Integrating Transformer for DCE-MRI Super Resolution Reconstruction, we call it HybridF. HybridF combines Transformer and CNN, where both global dependency and low-level spatial details can be efficiently captured. Moreover, where a Hybrid Fusion Block (HFB) is proposed to effectively aggregate multi-level features from Transformer and CNN. The method is evaluated by the data collection of clinical DCE-MRI and public IXI dataset. The DCE-MRI dataset consists of 212 cases of patients images. The reconstruction quality of our network is higher than that of the SRCNN, EDSR and RCAN method (＋2.24 dB, ＋2.79, ＋1.09). It is demonstrated that our network outperforms state-of-the art approaches it will contribute to the development of DCE-MRI super-resolution reconstruction.