Université Côte d'Azur and Inria Sophia-Antipolis Méditerranée Asclepios team, Inria Sophia Antipolis, France.
Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA.
Int J Comput Assist Radiol Surg. 2017 Sep;12(9):1543-1559. doi: 10.1007/s11548-016-1517-x. Epub 2017 Jan 17.
We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA).
The RFA computation becomes subject specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multimodal, pre- and postoperative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power.
To exploit this dataset, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved.
This enables an end-to-end preclinical validation framework that considers the available dataset.
我们旨在开发一个用于验证肝肿瘤射频消融（RFA）的特定于主体的多物理模型的框架。
在经过几个层次的个性化处理后，RFA 计算成为特定于主体的：几何形状和生物物理学（血液动力学、传热和扩展的细胞坏死模型）。我们提出了一种综合的实验设置，结合了多模态、术前和术后的解剖学和功能图像，以及术中信号的介入监测：温度和输送的功率。
为了利用这个数据集，引入了一种有效的处理管道，该管道可以处理图像噪声、可变分辨率和各向异性。验证研究包括来自五个健康猪肝脏的十二次消融：预测和实际消融范围之间的平均点到网格误差为 5.3 ± 3.6 毫米。
这使得能够实现一个端到端的临床前验证框架，该框架考虑了可用的数据集。
