Netherlands Cancer Institute, Department of Radiotherapy, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands.
Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
Comput Med Imaging Graph. 2024 Apr;113:102348. doi: 10.1016/j.compmedimag.2024.102348. Epub 2024 Feb 8.
Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.
递归推理机（RIM）是一种深度学习模型，它学习了一种迭代方案，用于对稀疏采样的 MRI 进行重建，已被证明在加速的 2D 和 3D MRI 扫描、从小数据集学习和对未见类型的数据很好地泛化方面表现出色。在这里，我们提出了动态递归推理机（DRIM），通过利用呼吸状态之间的相关性来重建稀疏采样的 4D MRI。DRIM 应用于基于重复交错冠状 2D 多切片 T 加权采集的肝脏病变的磁共振引导放射治疗的 4D 协议。我们通过消融研究证明，DRIM 优于 RIM，将 SSIM 评分从约 0.89 提高到 0.95。DRIM 允许扫描时间比当前的临床协议快约 2.7 倍，而图像锐度只有轻微损失。还可以使用切片位置之间的相关性，但发现其重要性较小，并且网络架构的大多数测试变体也不重要，只要网络处理呼吸状态。通过交叉验证，DRIM 在训练数据方面也表现出稳健性。我们进一步证明了在大范围的欠采样因子下的良好性能，并通过放射肿瘤学家的评估得出结论，在加速因子为 10x 和 8x 时，肝脏轮廓和内部结构的重建图像分别达到了临床可接受的标准。最后，我们表明，在重建之前根据呼吸状态对数据进行分箱处理会稍微降低重建质量，但会大大提高整个协议的速度。
