Tessema Abel Worku, Jin Seokha, Gong Yelim, Cho HyungJoon
Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, 105-222, 50, UNIST-Gil, Eonyang-Eup, Ulju-Gun, Ulsan, Republic of Korea.
School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Sci Rep. 2025 Mar 18;15(1):9383. doi: 10.1038/s41598-025-92493-9.
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 μm to 117 μm.
对比增强UTE-MRA可提供详细的血管造影信息，但代价是扫描时间延长，这可能会产生运动伪影，并影响对中风等时间敏感型疾病的诊断和治疗及时性。本研究旨在利用深度学习将快速采集的低分辨率UTE-MRA数据的分辨率提高到高分辨率。分别从健康对照和患中风的Wistar大鼠中收集了20例和10例对比增强3D UTE-MRA数据。实现了一种新设计的名为梯形残差密集生成器（LSRDG）的3D卷积神经网络以及其他先进模型（SR-ResNet、MRDG64），并在健康对照数据上进行训练和验证，在中风数据上进行测试。对于健康对照数据，使用我们提出的模型分别显著提高了结构相似性（SSIM）、峰值信噪比（PSNR）和均方误差（MSE）结果，分别为0.983、36.80和0.00021，而使用SR-ResNet时分别为0.964、34.38和0.00037，使用MRDG64时分别为0.978、35.47和0.00029。对于中风数据，使用我们提出的模型分别获得了0.963、34.14和0.00041的SSIM、PSNR和MSE分数，相比之下，使用SR-ResNet时分别为0.953、32.24和0.00061，使用MRDG64时分别为0.957、32.90和0.00054。此外，通过结合精心设计的网络、合适的损失函数以及使用较小图像块大小进行训练，对比增强UTE-MRA的分辨率从234μm显著提高到了117μm。
