Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network

Fetal brain magnetic resonance imaging (MRI) is becoming more important for early brain assessment in prenatal examination. Fast acquisition of three cross-sectional series/views is often used to eliminate motion effects using single-shot fast spin-echo sequences. Although stacked in 3D volumes, these slices are essentially 2D images with large slice thickness and distances (4 to 6 mm) resulting blurry multiplanar views. To better visualize and quantify fetal brains, it is desirable to reconstruct 3D images from different 2D cross-sectional series. In this paper, we present a super-resolution CNN-based network for 3D image reconstruction using unsupervised learning, referred to as cross-sectional image reconstruction (C-SIR). The key idea is that different cross-sectional images can help each other for training the C-SIR model. Additionally, existing high resolution data can also be used for pre-training the network in a supervised manner. In experiments, we show that such a network can be trained to reconstruct 3D images using simulated down-sampled adult images with much better image quality and image segmentation accuracy. Then, we illustrate that the proposed C-SIR approach generates relatively clear 3D fetal images than other algorithms.