3D-specific absorption rate estimation from high-intensity focused ultrasound sonications using the Green's function heat kernel.
3D-specific absorption rate estimation from high-intensity focused ultrasound sonications using the Green's function heat kernel.
复制标题
使用格林函数热核从高强度聚焦超声处理中估计 3D 特定吸收率。
DOI：
10.1002/mp.12978
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发表时间：
2018
期刊：
影响因子：
3.8
通讯作者：
Parker,DennisL
中科院分区：
文献类型：
--
作者：
Freeman,NicholasJ;Odéen,Henrik;Parker,DennisL
目的评估数值逆格林函数方法，该方法使用组织参数（热导率、比热容和质量密度）和三维 (3D) 磁共振成像 (MRI) 温度测量值从高强度聚焦超声 (HIFU) 超声处理中推导出比吸收率 (SAR)。方法使用模拟和 HIFU 超声处理的 MR 温度测量值来评估 SAR 估计值。对于模拟，使用超声模拟的混合角谱方法计算“真实”SAR。该“真实”SAR 被插入 Pennes 生物传热方程 (PBTE) 求解器中，以提供模拟温度图，然后使用所提出的方法计算 SAR 估计值。零平均高斯噪声（对应于 0.1 至 2.0°C 之间的温度精度）被添加到温度图中，以模拟各种体内情况。还使用了通过 3D 分段回波平面成像 MRI 脉冲序列监测的明胶体模中 HIFU 超声处理的实验 MR 温度图。为了确定模拟数据和虚拟数据的准确性，我们通过将估计的 SAR 插入 PBTE 求解器来重建温度图。在模拟和模型实验中，所提出的方法与之前发布的两种确定 SAR 的方法（线性方法和解析方法）进行了比较。所提出的数值方法同时利用了完整的 3D 数据，而之前发布的两种方法是逐层工作的。结果在没有噪声的情况下，从模拟加热剖面获得的 SAR 分布估计与初始真实 SAR 分布紧密匹配（在 10% 以内）。所得温度分布也与相应的初始温度分布密切匹配 (<0.2°C RMSE)。在存在温度测量噪声的情况下，SAR 分布的噪声被反卷积过程放大，而所得的温度分布仍然与初始“真实”温度分布紧密匹配。一般来说，观察到温度 RMSE 比添加噪声的水平高出大约 20-30%。相比之下，之前发布的线性方法对噪声不太敏感，但显着低估了 SAR。该解析方法对噪声也不太敏感，并且在中心平面上与 SAR 相匹配，但在纵向方向上大大低估了。从模型研究中也得到了类似的观察结果。所描述的数值逆格林函数方法非常快——比比较方法至少快两个数量级。结论所提出的数值逆格林函数方法计算速度快并且生成高精度的温度图。尽管普遍高估了真实 SAR 并放大了输入噪声，但情况确实如此。
PurposeTo evaluate a numerical inverse Green's function method for deriving specific absorption rates (SARs) from high‐intensity focused ultrasound (HIFU) sonications using tissue parameters (thermal conductivity, specific heat capacity, and mass density) and three‐dimensional (3D) magnetic resonance imaging (MRI) temperature measurements.MethodsSAR estimates were evaluated using simulations and MR temperature measurements from HIFU sonications. For simulations, a “true” SAR was calculated using the hybrid angular spectrum method for ultrasound simulations. This “true” SAR was plugged into a Pennes bioheat transfer equation (PBTE) solver to provide simulated temperature maps, which were then used to calculate the SAR estimate using the presented method. Zero mean Gaussian noise, corresponding to temperature precisions between 0.1 and 2.0°C, was added to the temperature maps to simulate a variety ofin vivosituations. Experimental MR temperature maps from HIFU sonications in a gelatin phantom monitored with a 3D segmented echo planar imaging MRI pulse sequence were also used. To determine the accuracy of the simulated and phantom data, we reconstructed temperature maps by plugging in the estimated SAR to the PBTE solver. In both simulations and phantom experiments, the presented method was compared to two previously published methods of determining SAR, a linear and an analytical method. The presented numerical method utilized the full 3D data simultaneously, while the two previously published methods work on a slice‐by‐slice basis.ResultsIn the absence of noise, SAR distribution estimates obtained from the simulated heating profiles match closely (within 10%) to the initial true SAR distribution. The resulting temperature distributions also match closely to the corresponding initial temperature distributions (<0.2°C RMSE). In the presence of temperature measurement noise, the SAR distributions have noise amplified by the inverse convolution process, while the resulting temperature distributions still match closely to the initial “true” temperature distributions. In general, temperature RMSE was observed to be approximately 20–30% higher than the level of the added noise. By contrast, the previously published linear method is less sensitive to noise, but significantly underpredicts the SAR. The analytic method is also less sensitive to noise and matches SAR in the central plane, but greatly underpredicts in the longitudinal direction. Similar observations are made from the phantom studies. The described numerical inverse Green's function method is very fast — at least two orders of magnitude faster than the compared methods.ConclusionThe presented numerical inverse Green's function method is computationally fast and generates temperature maps with high accuracy. This is true despite generally overestimating the true SAR and amplifying the input noise.