  Purpose: To acquire the real-time interactive temperature map for aqueous and
adipose tissue, the problems of long acquisition and processing time must be
addressed. To overcome these major challenges, this paper proposes a cascaded
convolutional 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 acquired
using a meGRE sequence; meGRE images with two flip angles (FAs) and meGRE
images with a single FA are acquired during the pretreatment and treatment
stages, respectively. These images are then processed and reconstructed by a
cascaded CNN, which consists of two CNNs. The first CNN (called DeepACCnet)
performs HR complex MR image reconstruction from the LR MR image acquired
during the treatment stage, which is improved by the HR magnitude MR image
acquired during the pretreatment stage. The second CNN (called DeepPROCnet)
copes with T1 mapping.
  Results: Measurements of temperature and T1 changes obtained by meGRE
combined with srVFA and cascaded CNNs were achieved in an agarose gel phantom,
ex vivo porcine muscle, and ex vivo porcine muscle with fat layers (heating
tests), and in vivo human prostate and brain (non-heating tests). In the
heating test, the maximum differences between fiber-optic sensor and samples
are less than 1 degree Celcius. In all cases, temperature mapping using the
cascaded CNN achieved the best results in all cases. The acquisition and
processing times for the proposed method are 0.8 s and 32 ms, respectively.
  Conclusions: Real-time interactive HR MR temperature mapping for
simultaneously measuring aqueous and adipose tissue is feasible by combining a
cascaded CNN with meGRE and srVFA.
