Kalman filtered MR temperature imaging for laser induced thermal therapies.
Kalman filtered MR temperature imaging for laser induced thermal therapies.
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DOI：
10.1109/tmi.2011.2181185
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发表时间：
2012-04
影响因子：
10.6
通讯作者：
Stafford RJ
中科院分区：
文献类型：
--
作者：
Fuentes D;Yung J;Hazle JD;Weinberg JS;Stafford RJ
在三维有限元卡尔曼滤波（KF）算法中使用随机形式的Pennes生物热模型的可行性进行了严格评估，以便在激光诱导热治疗（LITT）期间磁共振温度成像（MRTI）数据丢失时提供温度场估计的能力。通过系统地从临床mri引导的人脑LITT程序中删除时空信息，并将这些区域的预测与原始测量结果进行比较，分析了恢复缺失MRTI数据的能力。通过采集不确定性加权的温度误差的无量纲L2 （RMS）范数，对性能进行了定量评估。在没有数据损坏期间，观察到的误差历史表明，卡尔曼算法不会改变MR热成像提供的高质量温度测量。考虑的KF-MRTI实现可以在短时间内预测RMS误差< 4 （Δt < 10秒）的生物热传递，直到数据损坏消退。以目前的形式，KF-MRTI方法目前无法补偿连续的连续时间段的数据丢失Δt > 10sec。
The feasibility of using a stochastic form of Pennes bioheat model within a 3D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comparing predictions in these regions to the original measurements. Performance was quantitatively evaluated in terms of a dimensionless L2 (RMS) norm of the temperature error weighted by acquisition uncertainty. During periods of no data corruption, observed error histories demonstrate that the Kalman algorithm does not alter the high quality temperature measurement provided by MR thermal imaging. The KF-MRTI implementation considered is seen to predict the bioheat transfer with RMS error < 4 for a short period of time, Δt < 10sec, until the data corruption subsides. In its present form, the KF-MRTI method currently fails to compensate for consecutive for consecutive time periods of data loss Δt > 10sec.