Abstract
In magnetic resonance guided focused ultrasound (MRgFUS) brain applications the fully insonified field-of-view (FOV) is ideally monitored. This can be achieved by k-space subsampling and using a dedicated reconstruction method, such as the previously described model predictive filtering (MPF) method.[1] MPF utilizes the Pennes Bioheat transfer equation (PBTE) and tissue thermal and acoustic properties determined from a low-power pre-treatment heating (which ideally does not deliver any thermal dose, i.e. ΔT<2°C). The accuracy of the determined tissue parameters, and hence of the MPF reconstruction, depends on the low power heating. In this work we investigate dynamical adjustment of model parameters during heating for improved MPF temperature measurement accuracy.
Highlights
Background/introduction In magnetic resonance guided focused ultrasound (MRgFUS) brain applications the fully insonified field-ofview (FOV) is ideally monitored
Final adjustment Here the average values of Q and k achieved from all time-frames in 2) are used in the reconstruction
Temperature measurement accuracy was evaluated by investigating a local and a global root-mean-square-error (RMSE)
Summary
Background/introduction In magnetic resonance guided focused ultrasound (MRgFUS) brain applications the fully insonified field-ofview (FOV) is ideally monitored. 1. No adjustment The original implementation, using fixed values of k and Q[3,4] for all time-frames. 2. Best current estimate Implementation where Q from 1) is iteratively adjusted in each time-frame when the US is on, and k from 1) is iteratively adjusted when the US is off, so that the difference between the forward predicted model-only temperatures and the MPF estimates are minimized in each dynamic time-frame, figure 1.
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