Reddy Vishnu Vardhan Reddy Kanamata, Yogananda Chandan Ganesh Bangalore, Truong Nghi C D, Madhuranthakam Ananth J, Maldjian Joseph A, Fei Baowei
University of Texas at Dallas, Center for Imaging and Surgical Innovation, Richardson, TX.
University of Texas at Dallas, Department of Bioengineering, Richardson, TX.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12930. doi: 10.1117/12.3007743. Epub 2024 Apr 2.
The quality of brain MRI volumes is often compromised by motion artifacts arising from intricate respiratory patterns and involuntary head movements, manifesting as blurring and ghosting that markedly degrade imaging quality. In this study, we introduce an innovative approach employing a 3D deep learning framework to restore brain MR volumes afflicted by motion artifacts. The framework integrates a densely connected 3D U-net architecture augmented by generative adversarial network (GAN)-informed training with a novel volumetric reconstruction loss function tailored to 3D GAN to enhance the quality of the volumes. Our methodology is substantiated through comprehensive experimentation involving a diverse set of motion artifact-affected MR volumes. The generated high-quality MR volumes have similar volumetric signatures comparable to motion-free MR volumes after motion correction. This underscores the significant potential of harnessing this 3D deep learning system to aid in the rectification of motion artifacts in brain MR volumes, highlighting a promising avenue for advanced clinical applications.
脑部MRI容积的质量常常因复杂呼吸模式和非自主头部运动产生的运动伪影而受损，表现为模糊和重影，显著降低成像质量。在本研究中，我们引入了一种创新方法，采用3D深度学习框架来恢复受运动伪影影响的脑部MR容积。该框架集成了一个密集连接的3D U-net架构，通过基于生成对抗网络（GAN）的训练进行增强，并采用了一种针对3D GAN量身定制的新型容积重建损失函数，以提高容积质量。我们的方法通过涉及各种受运动伪影影响的MR容积的全面实验得到了验证。生成的高质量MR容积在运动校正后具有与无运动MR容积相似的容积特征。这突出了利用这种3D深度学习系统来帮助纠正脑部MR容积中运动伪影的巨大潜力，为先进的临床应用开辟了一条充满希望的途径。
