Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks

Purpose: Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time-consuming sequences while maintaining the image quality. Method: Fully sampled T1-FLAIR, T2-FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects. The T1-FLAIR and T2-FLAIR sequences were subsampled using Cartesian masks based on four different acceleration factors. The fully sampled T1/T2-FLAIR images were predicted from undersampled T1/T2-FLAIR images and T2WI images through deep learning-based reconstruction. They were qualitatively assessed by two senior radiologists in accordance with the diagnosis decision and a four-point scale image quality score. Furthermore, the images were quantitatively assessed based on regional signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs). The chi-square test was performed, where p < 0.05 indicated a difference with statistical significance. Results: The diagnosis decisions from two senior radiologists remained unchanged in accordance with the accelerated and fully sampled images. There were no significant differences in the regional SNRs and CNRs of most assessed regions (p > 0.05) between the accelerated and fully sampled images. Moreover, no significant difference was identified in the image quality assessed by two senior radiologists (p > 0.05). Conclusion: Deep learning-based image reconstruction is capable of significantly expediting the brain MR imaging process and producing acceptable image quality without affecting diagnosis decisions.