  Quantification of tissue parameters using MRI is emerging as a powerful tool
in clinical diagnosis and research studies. The need for multiple long scans
with different acquisition parameters prohibits quantitative MRI from reaching
widespread adoption in routine clinical and research exams. Accelerated
parameter mapping techniques leverage parallel imaging, signal modelling and
deep learning to offer more practical quantitative MRI acquisitions. However,
the achievable acceleration and the quality of maps are often limited. Joint
MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter
mapping technique with promising performance at high acceleration rates. It
synergistically combines parallel imaging, model-based and machine learning
approaches for joint mapping of T1, T2*, proton density and the field
inhomogeneity. However, Joint MAPLE suffers from prohibitively long
reconstruction time to estimate the maps from a multi-echo, multi-flip angle
(MEMFA) dataset at high resolution in a scan-specific manner. In this work, we
propose a faster version of Joint MAPLE which retains the mapping performance
of the original version. Coil compression, random slice selection,
parameter-specific learning rates and transfer learning are synergistically
combined in the proposed framework. It speeds-up the reconstruction time up to
700 times than the original version and processes a whole-brain MEMFA dataset
in 21 minutes on average, which originally requires ~260 hours for Joint MAPLE.
The mapping performance of the proposed framework is ~2-fold better than the
standard and the state-of-the-art evaluated reconstruction techniques on
average in terms of the root mean squared error.
