A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy.
A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy.
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DOI：
10.1002/mp.15542
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发表时间：
2022-05
期刊：
影响因子：
3.8
通讯作者：
Cai, Jing
中科院分区：
文献类型：
--
作者：
Xiao, Haonan;Ni, Ruiyan;Zhi, Shaohua;Li, Wen;Liu, Chenyang;Ren, Ge;Teng, Xinzhi;Liu, Weiwei;Wang, Weihu;Zhang, Yibao;Wu, Hao;Lee, Ho-Fun Victor;Cheung, Lai-Yin Andy;Chang, Hing-Chiu Charles;Li, Tian;Cai, Jing
大多数可用的4D-MRI技术受到图像质量不足和采集时间长的限制，或者需要特殊设计的序列或硬件，这些在临床上是不可用的。这些局限性极大地阻碍了4D-MRI的临床应用。本研究旨在利用市售的4D-MRI序列和双监督变形估计模型（DDEM）开发一种快速超高质量（UQ） 4D-MRI重建方法。纳入39例接受肝肿瘤放疗的患者。每位患者使用TWIST-VIBE MRI序列扫描以获得4D-MR图像。他们还接受了3D T1 / t2加权MRI扫描作为先验图像，任何时刻的UQ 4D-MRI都被认为是他们的变形。利用DDEM从4D- mri数据中获得4D可变形向量场（DVF），并利用该4D-DVF对先验图像进行变形，生成UQ 4D- mr图像。比较了ddm、VoxelMorph（归一化互相关（NCC）监督）、VoxelMorph（端到端点误差（EPE）监督）和参数总变差（pTV）算法的配准精度。使用感兴趣区域（ROI）跟踪误差定量评估UQ 4D-MRI上的肿瘤运动，而使用对比度-噪声比（CNR），肺-肝边缘清晰度和感知模糊度量（PBM）评估图像质量。ddm的配准精度显著优于VoxelMorph （NCC监督）、VoxelMorph （EPE监督）和pTV算法（均p < 0.001），推理时间为69.3±5.9 ms。UQ 4D-MRI在上下、前后和中外侧方向的ROI跟踪误差分别为0.79±0.65、0.50±0.55和0.51±0.58 mm。从原始4D-MRI到UQ 4D-MRI， CNR从7.25±4.89增加到18.86±15.81；肺肝边缘半最大全宽面内由8.22±3.17 mm降至3.65±1.66 mm，横面由8.79±2.78 mm降至5.04±1.67 mm， PBM由0.68±0.07降至0.38±0.01。该方法基于商业4D-MRI序列成功生成UQ 4D-MR图像。它对改善放射治疗期间肝脏肿瘤的运动管理显示出很大的希望。
Most available 4D-MRI techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI. This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM). Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a TWIST-VIBE MRI sequence to acquire 4D-MR images. They also received 3D T1-/T2-weighted MRI scans as prior images and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation (NCC) supervised), VoxelMorph (end-to-end point error (EPE) supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region-of-interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung–liver edge sharpness, and perceptual blur metric (PBM). The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised) and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior–inferior, anterior–posterior, and mid–lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung–liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy.
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