Abstract
Contrast-enhanced UTE-MRA provides detailed angiographic information but at the cost of prolonged scanning periods, which may impose moving artifacts and affect the promptness of diagnosis and treatment of time-sensitive diseases like stroke. This study aims to increase the resolution of rapidly acquired low-resolution UTE-MRA data to high-resolution using deep learning. A total of 20 and 10 contrast-enhanced 3D UTE-MRA data were collected from healthy control and stroke-bearing Wistar rats, respectively. A newly designed 3D convolutional neural network called ladder-shaped residual dense generator (LSRDG) and other state-of-the-art models (SR-ResNet, MRDG64) were implemented, trained, and validated on healthy control data and tested on stroke data. For healthy control data, significantly improved SSIM, PSNR, and MSE results were achieved using our proposed model, respectively 0.983, 36.80, and 0.00021, compared to 0.964, 34.38, and 0.00037 using SR-ResNet and 0.978, 35.47, and 0.00029 using MRDG64. For stroke data, respective SSIM, PSNR, and MSE scores of 0.963, 34.14, and 0.00041 were achieved using our proposed model compared to 0.953, 32.24, and 0.00061 (SR-ResNet) and 0.957, 32.90, and 0.00054 (MRDG64). Moreover, by combining a well-designed network, suitable loss function, and training with smaller patch sizes, the resolution of contrast-enhanced UTE-MRA was significantly improved from 234(3) μm(3) to 117(3) μm(3).