Harris Wendy, Yin Fang-Fang, Cai Jing, Ren Lei
Medical Physics Graduate Program, Duke University, Durham, NC, USA.
Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Quant Imaging Med Surg. 2020 Feb;10(2):432-450. doi: 10.21037/qims.2019.12.10.
The purpose of this study is to improve on-board volumetric cine magnetic resonance imaging (VC-MRI) using multi-slice undersampled cine images reconstructed using spatio-temporal k-space data, patient prior 4D-MRI, motion modeling (MM) and free-form deformation (FD) for real-time 3D target verification of liver and lung radiotherapy.
A previous method was developed to generate on-board VC-MRI by deforming prior MRI images based on a MM and a single-slice on-board 2D-cine image. The two major improvements over the previous method are: (I) FD was introduced to estimate VC-MRI to correct for inaccuracies in the MM; (II) multi-slice undersampled 2D-cine images reconstructed by a k-t SLR reconstruction method were used for FD-based estimation to maintain the temporal resolution while improving the accuracy of VC-MRI. The method was evaluated using XCAT lung simulation and four liver patients' data.
For XCAT, VC-MRI estimated using ten undersampled sagittal 2D-cine MRIs resulted in volume percent difference/volume dice coefficient/center-of-mass shift of 9.77%±3.71%/0.95±0.02/0.75±0.26 mm among all scenarios based on estimation with MM and FD. Adding FD optimization improved VC-MRI accuracy substantially for scenarios with anatomical changes. For patient data, the mean tumor tracking errors were 0.64±0.51, 0.62±0.47 and 0.24±0.24 mm along the superior-inferior (SI), anterior-posterior (AP) and lateral directions, respectively, across all liver patients.
It is feasible to improve VC-MRI accuracy while maintaining high temporal resolution using FD and multi-slice undersampled 2D cine images for real-time 3D target verification.
本研究的目的是利用时空 k 空间数据重建的多层欠采样电影图像、患者先前的 4D-MRI、运动建模(MM)和自由形式变形(FD)来改进机载容积电影磁共振成像(VC-MRI),以用于肝脏和肺部放疗的实时 3D 靶区验证。
之前开发了一种方法,通过基于 MM 和单一层面的机载 2D 电影图像对先前的 MRI 图像进行变形来生成机载 VC-MRI。与之前的方法相比,有两个主要改进:(I)引入 FD 来估计 VC-MRI,以纠正 MM 中的不准确之处;(II)使用 k-t SLR 重建方法重建的多层欠采样 2D 电影图像用于基于 FD 的估计,以在提高 VC-MRI 准确性的同时保持时间分辨率。使用 XCAT 肺部模拟和四名肝脏患者的数据对该方法进行了评估。
对于 XCAT,基于 MM 和 FD 估计,使用十个欠采样矢状面 2D 电影 MRI 估计的 VC-MRI 在所有场景中的体积百分比差异/体积骰子系数/质心偏移为 9.77%±3.71%/0.95±0.02/0.75±0.26 mm。添加 FD 优化在解剖结构发生变化的场景中显著提高了 VC-MRI 的准确性。对于患者数据,在所有肝脏患者中,肿瘤沿上下(SI)、前后(AP)和侧向方向的平均跟踪误差分别为 0.64±0.51、0.62±0.47 和 0.24±0.24 mm。
使用 FD 和多层欠采样 2D 电影图像在保持高时间分辨率的同时提高 VC-MRI 准确性以进行实时 3D 靶区验证是可行的。