MRI Superresolution Using Self Similarity and Image Priors
In Magnetic Resonance Imaging typical clinical settings, both low and high
resolution images of different types are routinarily acquired. In some
cases,
the acquired low resolution images have to be upsampled to match with
other high
resolution images for posterior analysis or postprocessing such as
registration
or multimodal segmentation. However, classical interpolation techniques
are not able to recover the high frequency information lost during the
acquisition process. In the present paper, a new superresolution method
is
proposed to reconstruct high resolution images from the low resolution
ones using
information from coplanar high resolution images acquired of the same
subject. Furthermore,
the reconstruction process is constrained to be physically plausible
with the MR
acquisition model that allows a meaningful interpretation of the
results. Experiments
on synthetic and real data are supplied to show the effectiveness of
the
proposed approach. A comparison with classical State-of-the-art
interpolation
techniques is presented to demonstrate the improved performance of the
proposed
methodology.
Details can be found in:
Manjón J.V., Coupé P., Buades A., Collins D.L., Robles M. MRI Superresolution Using Self Similarity and Image Priors. International Journal of Biomedical Imaging, Article ID 425891, 2010
Demo data and the source code of the proposed method can be found here.
Version 1.0 (May 2011)