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Longitudinal Guided Super-Resolution
Reconstruction of Neonatal Brain MR Images
Feng Shi
(
)
, Jian Cheng
(
)
, Li Wang, Pew-Thian Yap, and Dinggang Shen
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
{fengshi,dgshen}@med.unc.edu
Abstract.
Neonatal images have low spatial resolution and insufficient
tissue contrast. Generally, interpolation methods are used to upsample neonatal
images to a higher resolution for more effective image analysis. However, the
resulting images are often blurry and are susceptible to partial volume effect.
In this paper, we propose an algorithm that utilizes longitudinal prior informa-
tion for effective super-resolution reconstruction of neonatal images. We use a
non-local approach to learn the spatial relationships of brain structures in high-
resolution longitudinal images and apply this information to the super-
resolution reconstruction of the neonatal image. In other words, the recurring
patterns throughout the longitudinal scans are leveraged for reconstructing the
neonatal image with high resolution. To solve this otherwise ill-posed inverse
problem, low-rank and total-variation regularizations are enforced. Experiments
performed on both T1- and T2-weighted MR images of 28 neonates demon-
strate that the proposed method is capable of recovering more structural details
and outperforms methods such as nearest neighbor interpolation, spline-based
interpolation, non-local means upsampling, and both low-rank and total varia-
tion based super-resolution.
1
Introduction
*
Spatial resolution of neonatal magnetic resonance (MR) images is limited by diverse
factors such as imaging hardware, signal to noise ratio, and scanning time constraints
[1]. High-resolution (HR) images with small voxel size are often desired for greater
structural details [2]. In other words, images with low resolution (LR) are often af-
fected by partial volume effect (PVE), where a voxel captures signal from multiple
tissue types, resulting in fuzzy tissue boundaries [3]. This poses significant challenges
for subsequent image analysis, for example, in the assessment of volumetric and
shape changes of anatomical structures. PVE is especially severe in brain scans of
neonates, due to their small brain size and intrinsically low tissue signal contrast.
Interpolation methods are commonly used to upsample neonatal images to a higher
resolution before further analysis [4]. However note that each voxel in an LR image is
essentially a weighted average of corresponding voxels of a latent HR image. Thus, apply-
ing interpolation methods do not recover the HR image details with high frequency but
causes further blurring to the image by performing another round of averaging on the
F.S. and J.C. contributed equally to this work.
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