Zachiu C, Papadakis N, Ries M, Moonen C, Denis de Senneville B
Imaging Division, UMC Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.
Phys Med Biol. 2015 Dec 7;60(23):9003-29. doi: 10.1088/0031-9155/60/23/9003. Epub 2015 Nov 5.
Magnetic resonance (MR) guided high intensity focused ultrasound and external beam radiotherapy interventions, which we shall refer to as beam therapies/interventions, are promising techniques for the non-invasive ablation of tumours in abdominal organs. However, therapeutic energy delivery in these areas becomes challenging due to the continuous displacement of the organs with respiration. Previous studies have addressed this problem by coupling high-framerate MR-imaging with a tracking technique based on the algorithm proposed by Horn and Schunck (H and S), which was chosen due to its fast convergence rate and highly parallelisable numerical scheme. Such characteristics were shown to be indispensable for the real-time guidance of beam therapies. In its original form, however, the algorithm is sensitive to local grey-level intensity variations not attributed to motion such as those that occur, for example, in the proximity of pulsating arteries.In this study, an improved motion estimation strategy which reduces the impact of such effects is proposed. Displacements are estimated through the minimisation of a variation of the H and S functional for which the quadratic data fidelity term was replaced with a term based on the linear L(1)norm, resulting in what we have called an L(2)-L(1) functional.The proposed method was tested in the livers and kidneys of two healthy volunteers under free-breathing conditions, on a data set comprising 3000 images equally divided between the volunteers. The results show that, compared to the existing approaches, our method demonstrates a greater robustness to local grey-level intensity variations introduced by arterial pulsations. Additionally, the computational time required by our implementation make it compatible with the work-flow of real-time MR-guided beam interventions.To the best of our knowledge this study was the first to analyse the behaviour of an L(1)-based optical flow functional in an applicative context: real-time MR-guidance of beam therapies in moving organs.
磁共振（MR）引导的高强度聚焦超声和外照射放疗干预措施，我们将其称为束流疗法/干预措施，是用于腹部器官肿瘤无创消融的有前景的技术。然而，由于器官随呼吸不断位移，这些区域的治疗能量传递变得具有挑战性。先前的研究通过将高帧率MR成像与基于Horn和Schunck（H和S）提出的算法的跟踪技术相结合来解决这个问题，选择该算法是因为其收敛速度快且数值方案高度可并行化。这些特性被证明对于束流疗法的实时引导是不可或缺的。然而，该算法的原始形式对非运动引起的局部灰度强度变化敏感，例如在脉动动脉附近发生的那些变化。在本研究中，提出了一种改进的运动估计策略，该策略减少了此类影响的影响。通过最小化H和S泛函的变化来估计位移，其中二次数据保真项被基于线性L(1)范数的项所取代，从而产生了我们所称的L(2)-L(1)泛函。所提出的方法在两名健康志愿者的肝脏和肾脏在自由呼吸条件下进行了测试，数据集包含3000张图像，志愿者之间平均分配。结果表明，与现有方法相比，我们的方法对动脉搏动引入的局部灰度强度变化具有更高的鲁棒性。此外，我们实现所需的计算时间使其与实时MR引导的束流干预工作流程兼容。据我们所知，本研究是首次在应用背景下分析基于L(1)的光流泛函的行为：移动器官中束流疗法的实时MR引导。
