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Purpose: To acquire the real-time interactive temperature map for aqueous andadipose tissue, the problems of long acquisition and processing time must beaddressed. To overcome these major challenges, this paper proposes a cascadedconvolutional neural network (CNN) framework and multi-echo gradient echo(meGRE) with a single reference variable flip angle (srVFA).  Methods: To optimize the echo times for each method, MR images are acquiredusing a meGRE sequence; meGRE images with two flip angles (FAs) and meGREimages with a single FA are acquired during the pretreatment and treatmentstages, respectively. These images are then processed and reconstructed by acascaded CNN, which consists of two CNNs. The first CNN (called DeepACCnet)performs HR complex MR image reconstruction from the LR MR image acquiredduring the treatment stage, which is improved by the HR magnitude MR imageacquired during the pretreatment stage. The second CNN (called DeepPROCnet)copes with T1 mapping.  Results: Measurements of temperature and T1 changes obtained by meGREcombined with srVFA and cascaded CNNs were achieved in an agarose gel phantom,ex vivo porcine muscle, and ex vivo porcine muscle with fat layers (heatingtests), and in vivo human prostate and brain (non-heating tests). In theheating test, the maximum differences between fiber-optic sensor and samplesare less than 1 degree Celcius. In all cases, temperature mapping using thecascaded CNN achieved the best results in all cases. The acquisition andprocessing times for the proposed method are 0.8 s and 32 ms, respectively.  Conclusions: Real-time interactive HR MR temperature mapping forsimultaneously measuring aqueous and adipose tissue is feasible by combining acascaded CNN with meGRE and srVFA.

Real-time interactive magnetic resonance (MR) temperature imaging in  both aqueous and adipose tissues using cascaded deep neural networks for  MR-guided focused ultrasound surgery (MRgFUS)