PDF(Pubmed)
Abstract：
MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications.
We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI-Linac. The DCReconNet, CS-, PI-and UNet-based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported.
Imaging results demonstrated that DCReconNet better preserves image structures compared to CS- and PI-based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10-times faster than iterative, regularized reconstruction methods.
DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI-guided radiotherapy applications.
We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI-Linac. The DCReconNet, CS-, PI-and UNet-based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported.
Imaging results demonstrated that DCReconNet better preserves image structures compared to CS- and PI-based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10-times faster than iterative, regularized reconstruction methods.
DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI-guided radiotherapy applications.
摘要：
目的：由于其出色的软组织对比度和缺乏电离辐射，MRI越来越多地用于图像引导的放射治疗。然而,由梯度非线性(GNL)引起的几何畸变限制了解剖精度，可能影响肿瘤治疗的质量。此外,缓慢的MR采集和重建限制了有效图像引导的潜力。这里,我们展示了一种基于深度学习的方法，该方法可以从原始k空间数据中快速重建失真校正图像，用于MR引导的放射治疗应用。
方法：我们利用可解释展开网络的最新进展来开发失真校正重建网络（DDConecNet），该网络应用卷积神经网络（CNN）来学习有效的正则化和非均匀快速傅里叶变换用于GNL编码。DCReconNet在来自11名健康志愿者的公共MR大脑数据集上进行了全面采样和加速技术训练，包括并行成像(PI)和压缩传感(CS)。在幻影上测试了DCReconNet的性能，大脑,骨盆,和在1.0TMRI-直线加速器上获得的肺部图像。DCReconNet,CS-,通过结构相似性（SSIM）和RMS误差（RMSE）测量基于PI和UNet的重建图像质量，以进行数值比较。还报告了每种方法的计算时间和残余失真。
结果：成像结果表明，与基于CS和PI的重建方法相比，DCReconNet更好地保留了图像结构。DCReconNet在模拟脑图像上以四倍的加速度产生最高的SSIM(0.95中值)和最低的RMSE(<0.04)。DCReconNet比迭代快10倍以上，正则化重建方法。
结论：DCReconNet提供了快速和几何精确的图像重建，并具有用于MRI引导放射治疗应用的潜力。
方法：我们利用可解释展开网络的最新进展来开发失真校正重建网络（DDConecNet），该网络应用卷积神经网络（CNN）来学习有效的正则化和非均匀快速傅里叶变换用于GNL编码。DCReconNet在来自11名健康志愿者的公共MR大脑数据集上进行了全面采样和加速技术训练，包括并行成像(PI)和压缩传感(CS)。在幻影上测试了DCReconNet的性能，大脑,骨盆,和在1.0TMRI-直线加速器上获得的肺部图像。DCReconNet,CS-,通过结构相似性（SSIM）和RMS误差（RMSE）测量基于PI和UNet的重建图像质量，以进行数值比较。还报告了每种方法的计算时间和残余失真。
结果：成像结果表明，与基于CS和PI的重建方法相比，DCReconNet更好地保留了图像结构。DCReconNet在模拟脑图像上以四倍的加速度产生最高的SSIM(0.95中值)和最低的RMSE(<0.04)。DCReconNet比迭代快10倍以上，正则化重建方法。
结论：DCReconNet提供了快速和几何精确的图像重建，并具有用于MRI引导放射治疗应用的潜力。
