SOFNet: Optical-flow based large-scale slice augmentation of brain MRI

Abstract

Brain magnetic resonance imaging (MRI) data from multiple centers often exhibit variations in imaging conditions, such as the types of nuclear magnetic resonance instruments used and the presence of random noise. Additionally, discrepancies in the gap between MRI slices further complicate the usability of the data for advanced artificial intelligence (AI) analysis. Deep learning-based methods have emerged as practical solutions to address the challenge. However, existing research has largely overlooked the augmentation of brain MRI data, particularly when confronted with significant slice gaps, such as around 6 mm observed in our clinical brain MRI slices. In response to this research gap, we aim to develop novel approaches for augmenting brain MRI data, focusing on more significant slice gaps. To achieve this, we propose SOFNet, which utilizes the optical flow-based and encoder–decoder backbone. The primary objective of our model is to interpolate MRI slices while preserving feature consistency. Leveraging the optical flow method, which has exhibited exceptional performance compared to other super-resolution algorithms, our proposed approach has been evaluated on three distinct brain MRI datasets, explicitly addressing the gap between 4.2 mm and 6.0 mm. The experimental results highlight the significant enhancement in super-resolution quality achieved by SOFNet in generating adapted brain MRI data, surpassing other single-image super-resolution (SISR) methods in feature completion. To ensure the credibility of the interpolated brain MRI slices, we conducted experiments on three axes of MRI based on metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These experiments demonstrate the effectiveness of our approach in transforming low-resolution MRI data into clear and reliable brain MRIs, thereby enabling improved analysis using AI techniques.