PDF(Pubmed)
Abstract：
OBJECTIVE: To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI.
METHODS: Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers.
RESULTS: The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts.
CONCLUSIONS: Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.
METHODS: Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers.
RESULTS: The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts.
CONCLUSIONS: Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.
摘要：
目的：为4D-MRI重建开发一种新的深度学习方法，名为Movienet,它利用时空线圈相关性和运动保存，而不是k空间数据一致性，以加速金角径向数据的采集，并在动态MRI中实现亚秒重建时间。
方法：Movienet使用U-net架构，具有完全在图像域中操作的修改的残差学习块，以去除混叠伪影并重建非混叠的运动分辨4D图像。通过对输入图像和参考进行分类以从到期到吸气的线性运动顺序进行训练来强制保持运动。以低于用于训练的参考XD-GRASP图像的扫描时间收集输入图像。Movienet在治疗性1.5TMR-Linac（1.5倍采集加速度）和诊断性3TMRI扫描仪（4D和3D的2倍和2.25倍采集加速度，分别)。专家临床读者对图像质量进行了定量和定性评估。
结果：Movienet的重建时间为0.69s（4种运动状态）和0.75s（10种运动状态），大大低于迭代XD-GRASP和展开重建网络。Movienet实现比XD-GRASP更快的采集，具有相似的整体图像质量和改进的条纹伪影抑制。
结论：Movienet相对于压缩传感加速数据采集，并在不到1s的时间内重建4D图像，这将使得能够在临床环境中有效实施4DMRI,用于快速抗运动3D解剖成像或运动分辨4D成像。
方法：Movienet使用U-net架构，具有完全在图像域中操作的修改的残差学习块，以去除混叠伪影并重建非混叠的运动分辨4D图像。通过对输入图像和参考进行分类以从到期到吸气的线性运动顺序进行训练来强制保持运动。以低于用于训练的参考XD-GRASP图像的扫描时间收集输入图像。Movienet在治疗性1.5TMR-Linac（1.5倍采集加速度）和诊断性3TMRI扫描仪（4D和3D的2倍和2.25倍采集加速度，分别)。专家临床读者对图像质量进行了定量和定性评估。
结果：Movienet的重建时间为0.69s（4种运动状态）和0.75s（10种运动状态），大大低于迭代XD-GRASP和展开重建网络。Movienet实现比XD-GRASP更快的采集，具有相似的整体图像质量和改进的条纹伪影抑制。
结论：Movienet相对于压缩传感加速数据采集，并在不到1s的时间内重建4D图像，这将使得能够在临床环境中有效实施4DMRI,用于快速抗运动3D解剖成像或运动分辨4D成像。
