Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
Computational Imaging Group for MR therapy & Diagnostics, University Medical Center Utrecht, Utrecht, The Netherlands.
Phys Med Biol. 2023 Jul 5;68(14). doi: 10.1088/1361-6560/ace023.
The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments.We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference.The framework is demonstrated in 2D on a motion phantom, andon volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated by3D experiments with a digital motion phantom. The framework was compared with template matching-a reference, image-based, method-and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement.The high accuracy in combination with a total latency of less than 10 ms-including data acquisition and processing-make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments.
心肺运动的高速给心脏立体定向放射消融（STAR）治疗带来了独特的挑战，因为 MR 直线加速器需要以最大延迟 100 毫秒的速度跟踪心肌标志点，这其中包括所需数据的获取。本研究旨在提出一种新方法，该方法允许从 MRI 数据的少量读取中跟踪心肌标志点，从而实现足够用于 STAR 治疗的延迟。
我们提出了一种跟踪框架，该框架仅需要少量 k 空间数据作为输入，而这些数据的获取速度可以比 MR 图像快至少一个数量级。结合称为高斯过程的概率机器学习框架的实时跟踪速度，这使得可以以足够低的延迟跟踪心肌标志点，包括获取所需数据和跟踪推断。
该框架在运动体模、志愿者和室性心动过速（心律失常）患者上进行了二维演示，并且通过数字运动体模的 3D 实验证明了扩展到 3D 的可行性。该框架与模板匹配（一种基于图像的参考方法）和线性回归方法进行了比较。结果表明，与替代方法相比，所提出的框架的总延迟（<10 毫秒）低一个数量级。对于所有实验，与参考跟踪方法的均方根距离和平均端点距离小于 0.8 毫米，显示出极好的（亚像素）一致性。
高精度与小于 10 毫秒的总延迟（包括数据采集和处理）相结合，使得该方法成为 STAR 治疗过程中跟踪的合适候选者。此外，高斯过程的概率性质还可以访问实时预测不确定性，这对于治疗过程中的实时质量保证可能很有用。
