Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors
Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors
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DOI：
10.1109/tmi.2015.2406575
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发表时间：
2015-07-01
影响因子：
10.6
通讯作者：
Comaniciu, Dorin
中科院分区：
文献类型：
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作者：
Audigier, Chloe;Mansi, Tommaso;Comaniciu, Dorin
射频消融（RFA）是一种治疗肝癌的既定治疗方法，当切除术是不可能的。然而，其最佳递送受到大血管的存在和生物组织随时间变化的热导率的挑战。因此，不完全治疗和复发风险增加是常见的。因此，需要一种能够准确规划RFA的工具。该手稿描述了一种基于格子玻尔兹曼方法（LBM）和患者特异性术前图像计算所需消融程度的新方法。从体积图像获得肝脏的详细解剖模型。然后，采用热扩散、细胞坏死和通过血管和肝脏的血流的计算模型来计算给定探针位置、消融持续时间和生物参数的消融组织的程度。该模型进行了验证，对分析解决方案，表现出良好的保真度。我们还评估了所提出的框架对10名接受RFA的患者的预测能力，这些患者的术前和术后图像可用。计算出的消融范围与术后图像中观察到的真实情况之间的比较是有希望的（DICE指数：42%，灵敏度：67%，阳性预测值：38%）。还强调了在模拟电加热消融时考虑肝脏灌注的重要性。在图形处理单元（GPU）上实现，我们的方法在1.14分钟内模拟1分钟的消融，允许近实时计算。
Radiofrequency ablation (RFA) is an established treatment for liver cancer when resection is not possible. Yet, its optimal delivery is challenged by the presence of large blood vessels and the time-varying thermal conductivity of biological tissue. Incomplete treatment and an increased risk of recurrence are therefore common. A tool that would enable the accurate planning of RFA is hence necessary. This manuscript describes a new method to compute the extent of ablation required based on the Lattice Boltzmann Method (LBM) and patient-specific, pre-operative images. A detailed anatomical model of the liver is obtained from volumetric images. Then a computational model of heat diffusion, cellular necrosis, and blood flow through the vessels and liver is employed to compute the extent of ablated tissue given the probe location, ablation duration and biological parameters. The model was verified against an analytical solution, showing good fidelity. We also evaluated the predictive power of the proposed framework on ten patients who underwent RFA, for whom pre-and post-operative images were available. Comparisons between the computed ablation extent and ground truth, as observed in postoperative images, were promising (DICE index: 42%, sensitivity: 67%, positive predictive value: 38%). The importance of considering liver perfusion while simulating electrical-heating ablation was also highlighted. Implemented on graphics processing units (GPU), our method simulates 1 minute of ablation in 1.14 minutes, allowing near real-time computation.