Brain Segmentation from Super-Resolved Magnetic Resonance Images

The objective of this work is to investigate the ability of a 2D super resolution (SR) technique in 3D restoration and enhancement of brain magnetic resonance images to facilitate the study of cerebral aging bio-markers. The SR method exploits the joint properties of the system point spread function and sub-sampling operators to derive a fast algorithm. Brain images of the common marmoset, Callithrix jacchus, acquired at different ages are used in this study. The evaluation of the final outcome of our method is done by computing the intracranial volume from the segmentation of the brain compartments: gray matter, white matter and cerebrospinal fluid. Results show that the deblurring of the images improves the segmentation process with respect to the ground truth. However, super resolution leads to the best quantification of the intracranial volume when compared to the deblurred and the original images. Therefore, despite its sub-optimality, the 2D SR method provides reliable results for improving the quality of the images used in the study of aging in terms of precision of reconstruction and computational time.

[1]  E T Bullmore,et al.  Trajectories and Milestones of Cortical and Subcortical Development of the Marmoset Brain From Infancy to Adulthood , 2018, Cerebral cortex.

[2]  Jens C. Pruessner,et al.  Regional Frontal Cortical Volumes Decrease Differentially in Aging: An MRI Study to Compare Volumetric Approaches and Voxel-Based Morphometry , 2002, NeuroImage.

[3]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[4]  S. Tardif,et al.  The marmoset as a model of aging and age-related diseases. , 2011, ILAR journal.

[5]  Michael K. Ng,et al.  Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods , 2010, SIAM J. Sci. Comput..

[6]  Eric Van Reeth,et al.  Super-resolution in magnetic resonance imaging: A review , 2012 .

[7]  Jean-Yves Tourneret,et al.  Fast Single Image Super-Resolution Using a New Analytical Solution for $\ell _{2}$ – $\ell _{2}$ Problems , 2016, IEEE Transactions on Image Processing.

[8]  Faith M. Gunning-Dixon,et al.  Aging of cerebral white matter: a review of MRI findings , 2009, International journal of geriatric psychiatry.

[9]  Laurent Risser,et al.  In vivo localization of cortical areas using a 3D computerized atlas of the marmoset brain , 2019, Brain Structure and Function.

[10]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[11]  W. Eric L. Grimson,et al.  A Bayesian model for joint segmentation and registration , 2006, NeuroImage.

[12]  Jean-Yves Tourneret,et al.  Fast Single Image Super-Resolution , 2015, ArXiv.

[13]  Jean-Yves Tourneret,et al.  Joint Bayesian deconvolution and pointspread function estimation for ultrasound imaging , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[14]  Jean-Yves Tourneret,et al.  A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT , 2018, IEEE Transactions on Medical Imaging.

[15]  Jean-Yves Tourneret,et al.  Enhancement of 250-MHz quantitative acoustic-microscopy data using a single-image super-resolution method , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[16]  Adrian Basarab,et al.  On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  Cheryl L. Dahle,et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. , 2005, Cerebral cortex.