MRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real-time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation. Here, we demonstrate the use of automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction. We find that AUTOMAP-reconstructed radial k-space has equivalent accuracy to CS but with much shorter processing times after initial fine-tuning on retrospectively acquired lung cancer patient data. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy. Our work shows that AUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to existing approaches for real-time image guidance applications.
翻译:对辐射束进行动态调整以实时跟踪肿瘤运动的辐射光束的MRI指导技术将带来更准确的癌症治疗和减少附带健康组织损害。重建未充分抽样的MR数据的金标准是压缩感测(CS),该标准计算缓慢,并限制图像可用于实时适应的速率。在这里,我们展示了使用多光速自动变换(AUTOMAP),该通用框架将MW原始信号映射到目标图像域,以迅速从未充分抽样的放射K-空间数据重建图像。AUTOMAP神经网络接受了培训,以从一个金形的辐射应用获取图像,一个运动敏感成像、肺癌病人数据和图像网络通用图像的基准,重建图像。随后,模型培训又增加了从YouTube-8M数据集的视频中提取的移动编码K-空间数据,鼓励动态重建。我们发现AUTOMAP重新构建的这些辐射K-空间信号相当于CS的准确度,但在对追溯性获得的肺癌感应感应指示的图像应用初步调整后,处理时间要短得多,从追溯获得的升级的神经-直观感应变现的神经-运动-运动-运动的校正运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动性运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动-运动