  Purpose: In many clinical MRI scenarios, existing imaging information can be
used to significantly shorten acquisition time or to improve Signal to Noise
Ratio (SNR). In this paper the authors present a framework for fast MRI by
exploiting a reference image (FASTMER).
  Methods: The proposed approach utilizes the possible similarity of the
reference image that exists in many clinical MRI imaging scenarios. Such
scenarios include similarity between adjacent slices in high resolution MRI,
similarity between various contrasts in the same scan and similarity between
different scans of the same patient. The authors take into account that the
reference image may exhibit low similarity with the acquired image and develop
an iterative weighted approach for reconstruction, which tunes the weights
according to the degree of similarity.
  Results: Experimental results demonstrate the performance of the method in
three different clinical MRI scenarios: SNR improvement in high resolution
brain MRI, exploiting similarity between T2-weighted and fluid-attenuated
inversion recovery (FLAIR) for fast FLAIR scanning and utilizing similarity
between baseline and follow-up scans for fast follow-up. Results outperform
reconstruction results of existing state-of-the-art methods.
  Conclusions: The authors present a method for fast MRI by exploiting a
reference image. The method is based on an iterative reconstruction approach
that supports cases in which similarity to the reference scan is not
guaranteed, which enables the applicability of the method to a variety of MRI
applications. Thanks to the existence of reference images in various clinical
imaging scenarios, the proposed framework can play a major part in improving
reconstruction in many MR applications.
