Development and Evaluation of Deep Learning-Based Reconstruction Using Preclinical 7T Magnetic Resonance Imaging

Abstract

This study investigated a method for improving the quality of images with a low number of excitations (NEXs) based on deep learning using T2-weighted magnetic resonance imaging (MRI) of the heads of normal Wistar rats to achieve higher image quality and a shorter acquisition time. A 7T MRI was used to acquire T2-weighted images of the whole brain with NEXs = 2, 4, 8, and 12. As a preprocessing step, non-rigid registration of the acquired low NEX images (NEXs = 2, 4, 8) and NEXs = 12 images was performed. A residual dense network (RDN) was used for training. A low NEX image was used as the input to the RDN, and the NEX12 image was used as the correct image. For quantitative evaluation, we measured the signal-to-noise ratio (SNR), peak SNR, and structural similarity index measure of the original image and the image obtained by RDN. The NEX2 results are presented as an example. The SNR of the cortex was 10.4 for NEX2, whereas the SNR of the image reconstructed with RDN for NEX2 was 32.1. (The SNR NEX12 was 19.6) In addition, the PSNR in NEX2 was significantly increased to 35.4 ± 2.0 compared to the input image and to 37.6 ± 2.9 compared to the reconstructed image (p = 0.05). The SSIM in NEX2 was 0.78 ± 0.05 compared to the input image and 0.91 ± 0.05 compared to the reconstructed image (p = 0.0003). Furthermore, NEX2 succeeded in reducing the shooting time by 83%. Therefore, in preclinical 7T MRI, supervised learning between the NEXs using RDNs can potentially improve the image quality of low NEX images and shorten the acquisition time.