Terpstra Maarten L, Maspero Matteo, Verhoeff Joost J C, van den Berg Cornelis A T
Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Med Phys. 2023 Sep;50(9):5331-5342. doi: 10.1002/mp.16643. Epub 2023 Aug 1.
Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and reconstruction times.
To develop a deep learning architecture to quickly acquire and reconstruct high-quality 4D-MRI, enabling accurate motion quantification for MRI-guided radiotherapy (MRIgRT).
A small convolutional neural network called MODEST is proposed to reconstruct 4D-MRI by performing a spatial and temporal decomposition, omitting the need for 4D convolutions to use all the spatio-temporal information present in 4D-MRI. This network is trained on undersampled 4D-MRI after respiratory binning to reconstruct high-quality 4D-MRI obtained by compressed sensing reconstruction. The network is trained, validated, and tested on 4D-MRI of 28 lung cancer patients acquired with a T1-weighted golden-angle radial stack-of-stars (GA-SOS) sequence. The 4D-MRI of 18, 5, and 5 patients were used for training, validation, and testing. Network performances are evaluated on image quality measured by the structural similarity index (SSIM) and motion consistency by comparing the position of the lung-liver interface on undersampled 4D-MRI before and after respiratory binning. The network is compared to conventional architectures such as a U-Net, which has 30 times more trainable parameters.
MODEST can reconstruct high-quality 4D-MRI with higher image quality than a U-Net, despite a thirty-fold reduction in trainable parameters. High-quality 4D-MRI can be obtained using MODEST in approximately 2.5 min, including acquisition, processing, and reconstruction.
High-quality accelerated 4D-MRI can be obtained using MODEST, which is particularly interesting for MRIgRT.
呼吸分辨四维磁共振成像（4D-MRI）为移动肿瘤的精确放射治疗提供了重要的运动信息。然而，获取高质量的4D-MRI存在采集和重建时间长的问题。
开发一种深度学习架构，以快速采集和重建高质量的4D-MRI，从而实现MRI引导放射治疗（MRIgRT）的精确运动量化。
提出了一种名为MODEST的小型卷积神经网络，通过进行空间和时间分解来重建4D-MRI，无需使用4D卷积来利用4D-MRI中存在的所有时空信息。该网络在呼吸分箱后的欠采样4D-MRI上进行训练，以重建通过压缩感知重建获得的高质量4D-MRI。该网络在28例肺癌患者的4D-MRI上进行训练、验证和测试，这些4D-MRI是使用T1加权黄金角径向星状堆叠（GA-SOS）序列采集的。18例、5例和5例患者的4D-MRI分别用于训练、验证和测试。通过结构相似性指数（SSIM）测量图像质量，并通过比较呼吸分箱前后欠采样4D-MRI上肺-肝界面的位置来评估运动一致性，以此来评估网络性能。将该网络与传统架构（如U-Net）进行比较，U-Net的可训练参数多30倍。
尽管可训练参数减少了30倍，但MODEST仍能重建出比U-Net更高质量的4D-MRI。使用MODEST可以在大约2.5分钟内获得高质量的4D-MRI，包括采集、处理和重建。
使用MODEST可以获得高质量的加速4D-MRI，这对于MRIgRT尤其有意义。
