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首页> 外文期刊>International journal of numerical methods for heat & fluid flow >State estimation problems in PRF-shift magnetic resonance thermometry
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State estimation problems in PRF-shift magnetic resonance thermometry

机译：PRF位移磁共振测温中的状态估计问题

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Purpose - The purpose of this paper is to apply the Steady State Kalman Filter for temperature measurements of tissues via magnetic resonance thermometry. Instead of using classical direct inversion, a methodology is proposed that couples the magnetic resonance thermometry with the bioheat transfer problem and the local temperatures can be identified through the solution of a state estimation problem. Design/methodology/approach - Heat transfer in the tissues is given by Pennes' bioheat transfer model, while the Proton Resonance Frequency (PRF)-Shift technique is used for the magnetic resonance thermometry. The problem of measuring the transient temperature field of tissues is recast as a state estimation problem and is solved through the Steady-State Kalman filter. Noisy synthetic measurements are used for testing the proposed methodology. Findings - The proposed approach is more accurate for recovering the local transient temperatures from the noisy PRF-Shift measurements than the direct data inversion. The methodology used here can be applied in real time due to the reduced computational cost Idealized test cases are examined that include the actual geometry of a forearm. Research limitations/implications - The solution of the state estimation problem recovers the temperature variations in the region more accurately than the direct inversioa Besides that, the estimation of the temperature field in the region was possible with the solution of the state estimation problem via the Steady-State Kalman filter, but not with the direct inversion. Practical implications - The recursive equations of the Steady-State Kalman filter can be calculated in computational times smaller than the supposed physical times, thus demonstrating that the present approach can be used for real-time applications, such as in control of the heating source in the hyperthermia treatment of cancer. Originality/value - The original and novel contributions of the manuscript include: formulation of the PRF-Shift thermometry as a state estimation problem, which results in reduced uncertainties of the temperature variation as compared to the classical direct inversion; estimation of the actual temperature in the region with the solution of the state estimation problem, which is not possible with the direct inversion that is limited to the identification of the temperature variation; solution of the state estimation problem with the Steady-State Kalman filter, which allows for fast computations and real-time calculations.
机译：目的-本文的目的是将稳态卡尔曼滤波器应用于通过磁共振测温法测量组织的温度。代替使用经典的直接反演，提出了一种方法，该方法将磁共振测温与生物传热问题耦合，并且可以通过状态估计问题的解决方案来确定局部温度。设计/方法/方法-组织中的热传递由Pennes的生物热传递模型给出，而质子共振频率（PRF）-Shift技术用于磁共振测温。测量组织的瞬态温度场的问题被重铸为状态估计问题，并通过稳态卡尔曼滤波器解决。嘈杂的综合测量用于测试所提出的方法。结果-与直接的数据反演相比，从带噪的PRF-Shift测量中，所提出的方法更准确地恢复了局部瞬态温度。由于降低了计算成本，因此可以实时应用此处使用的方法，可以检查理想的测试用例，其中包括前臂的实际几何形状。研究局限/意义-状态估计问题的解决方案比直接逆过程更准确地恢复了区域中的温度变化，此外，通过稳态解决状态估计问题，可以对该区域中的温度场进行估计-状态卡尔曼滤波器，但不使用直接反演。实际意义-稳态卡尔曼滤波器的递归方程可以用比假定的物理时间小的计算时间来计算，从而证明本方法可用于实时应用，例如控制加热源。热疗治疗癌症。原创性/价值-该手稿的原始和新颖贡献包括：将PRF-Shift测温公式表示为状态估计问题，与传统的直接反演相比，该方法可降低温度变化的不确定性；通过状态估计问题的解决方案来估计该区域中的实际温度，而这仅限于识别温度变化的直接反演是不可能的；稳态卡尔曼滤波器可解决状态估计问题，可实现快速计算和实时计算。

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