Ginn John, Wang Chunhao, Yang Deshan
Department of Radiation Oncology, Duke Cancer Center, Duke University, Durham, North Carolina, USA.
Med Phys. 2025 Mar;52(3):1390-1398. doi: 10.1002/mp.17556. Epub 2024 Dec 3.
In magnetic resonance image (MRI)-guided radiotherapy (MRgRT), 2D rapid imaging is commonly used to track moving targets with high temporal frequency to minimize gating latency. However, anatomical motion is not constrained to 2D, and a portion of the target may be missed during treatment if 3D motion is not evaluated. While some MRgRT systems attempt to capture 3D motion by sequentially tracking motion in 2D orthogonal imaging planes, this approach assesses 3D motion via independent 2D measurements at alternating instances, lacking a simultaneous 3D motion assessment in both imaging planes.
We hypothesized that a motion model could be derived from prior 2D orthogonal imaging to estimate 3D motion in both planes simultaneously. We present a manifold learning technique to estimate 3D motion from 2D orthogonal imaging.
Five healthy volunteers were scanned under an IRB-approved protocol using a 3.0 T Siemens Skyra simulator. Images of the liver dome were acquired during free breathing (FB) with a 2.6 mm × 2.6 mm in-plane resolution for approximately 10 min in alternating sagittal and coronal planes at ∼5 frames per second. The motion model was derived using a combined manifold learning and alignment approach based on locally linear embedding (LLE). The model utilized the spatially overlapping MRI signal shared by both imaging planes to group together images that had similar signals, enabling motion estimation in both planes simultaneously. The model's motion estimates were compared to the ground truth motion derived in each newly acquired image using deformable registration. A simulated target was defined on the dome of the liver and used to evaluate model performance. The Dice similarity coefficient and distance between the model-tracked and image-tracked contour centroids were evaluated. Motion modeling error was estimated in the orthogonal plane by back-propagating the motion to the currently imaged plane and by interpolating the motion between image acquisitions where ground truth motion was available.
The motion observed in the healthy volunteer studies ranged from 12.6 to 38.7 mm. On average, the model demonstrated sub-millimeter precision and > 0.95 Dice coefficient compared to the ground truth motion observed in the currently imaged plane. The average Dice coefficient and centroid distance between the model-tracked and ground truth target contours were 0.96 ± 0.03 and 0.26 mm ± 0.27 mm respectively across all volunteer studies. The out-of-plane centroid motion error was estimated to be 0.85 mm ± 1.07 mm and 1.26 mm ± 1.38 mm using the back-propagation (BP) and interpolation error estimation methods.
The healthy volunteer studies indicate promising results using the proposed motion modeling technique. Out-of-plane modeling error was estimated to be higher but still demonstrated sub-voxel motion accuracy.
在磁共振成像（MRI）引导的放射治疗（MRgRT）中，二维快速成像通常用于以高时间频率跟踪移动目标，以最小化门控延迟。然而，解剖运动并不局限于二维，如果不评估三维运动，在治疗过程中可能会遗漏部分目标。虽然一些MRgRT系统试图通过在二维正交成像平面中顺序跟踪运动来捕获三维运动，但这种方法是通过在交替实例下的独立二维测量来评估三维运动的，在两个成像平面中缺乏同时的三维运动评估。
我们假设可以从先前的二维正交成像中导出一个运动模型，以同时估计两个平面中的三维运动。我们提出了一种流形学习技术，用于从二维正交成像中估计三维运动。
在一项经机构审查委员会（IRB）批准的方案下，使用3.0T西门子Skyra模拟器对五名健康志愿者进行扫描。在自由呼吸（FB）期间采集肝穹窿的图像，平面分辨率为2.6mm×2.6mm，每秒约5帧，在矢状面和冠状面交替采集约10分钟。使用基于局部线性嵌入（LLE）的组合流形学习和对齐方法导出运动模型。该模型利用两个成像平面共享的空间重叠MRI信号，将具有相似信号的图像分组在一起，从而能够同时在两个平面中进行运动估计。使用可变形配准将模型的运动估计与每个新采集图像中得出的真实运动进行比较。在肝穹窿上定义一个模拟目标，并用于评估模型性能。评估了模型跟踪和图像跟踪轮廓质心之间的骰子相似系数和距离。通过将运动反向传播到当前成像平面并在有真实运动的图像采集之间内插运动，在正交平面中估计运动建模误差。
在健康志愿者研究中观察到的运动范围为12.6至38.7mm。平均而言，与当前成像平面中观察到的真实运动相比，该模型显示出亚毫米级的精度和>0.95的骰子系数。在所有志愿者研究中，模型跟踪和真实目标轮廓之间的平均骰子系数和质心距离分别为0.96±0.03和0.26mm±0.27mm。使用反向传播（BP）和内插误差估计方法估计平面外质心运动误差分别为0.85mm±1.07mm和1.26mm±1.38mm。
健康志愿者研究表明，使用所提出的运动建模技术有良好的结果。平面外建模误差估计较高，但仍显示出亚体素运动精度。
