Image Quality Assessment using an Orientation Recognition Network for Fetal MRI

Motivation: Fetal MRI is important in clinical and scientific applications but prone to motion artifacts. Automated image quality assessment (IQA) assists data acquisition and subsequent analyses. However, training neural networks for IQA requires labor-intensive manual annotation. Goal(s): To develop a model for fetal MRI IQA that doesn't require image quality labels. Approach: A network is trained to determine the acquisition orientation of 2D T2-weighted images. The variation of orientation recognition network (ORN) inferences for central images of a brain stack is used to assess motion and the image quality. Results: High-quality and low-quality images are robustly discriminated. Image super-resolution from brain stacks is improved. Impact: ORN-IQA eradicates the necessity image quality labels for training, thereby circumventing manual annotation. ORN-IQA simplifies online image quality evaluation and permits image reacquisition during fetal MR scans. Moreover, ORN-IQA improves super-resolution reconstruction results.