MRI-guided laser-interstitial thermal therapy (MRgLITT) is a minimally invasive therapeutic method in neurosurgery. Accelerating the data acquisition of thermometry for MRgLITT treatment is crucial in achieving high temporal-spatial resolution and large volume coverage. In this work, we suggest using the convolutional recurrent neural network (CRNN) to achieve real-time reconstruction for accelerated temperature mapping, because CRNN is capable of utilizing the temporal correlations of dynamic data to resolve aliasing artifacts. Results demonstrate that 6-fold acceleration can be achieved in MRgLITT treatment using CRNN with clinically acceptable reconstruction time and temperature measurement errors.
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