Diffusion-Weighted Images Superresolution Using High-Order SVD

The spatial resolution of diffusion-weighted imaging (DWI) is limited by several physical and clinical considerations, such as practical scanning times. Interpolation methods, which are widely used to enhance resolution, often result in blurred edges. Advanced superresolution scanning acquires images with specific protocols and long acquisition times. In this paper, we propose a novel single image superresolution (SR) method which introduces high-order SVD (HOSVD) to regularize the patch-based SR framework on DWI datasets. The proposed method was implemented on an adaptive basis which ensured a more accurate reconstruction of high-resolution DWI datasets. Meanwhile, the intrinsic dimensional decreasing property of HOSVD is also beneficial for reducing the computational burden. Experimental results from both synthetic and real DWI datasets demonstrate that the proposed method enhances the details in reconstructed high-resolution DWI datasets and outperforms conventional techniques such as interpolation methods and nonlocal upsampling.

[1]  Anand Rangarajan,et al.  Image Denoising Using the Higher Order Singular Value Decomposition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[3]  Jung-Lung Hsu,et al.  Microstructural white matter changes in normal aging: A diffusion tensor imaging study with higher-order polynomial regression models , 2010, NeuroImage.

[4]  D. Parker,et al.  Analysis of partial volume effects in diffusion‐tensor MRI , 2001, Magnetic resonance in medicine.

[5]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[6]  Tipu Z. Aziz,et al.  Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner , 2011, NeuroImage.

[7]  François Rousseau,et al.  A non-local approach for image super-resolution using intermodality priors , 2010, Medical Image Anal..

[8]  L. Lathauwer,et al.  Signal Processing based on Multilinear Algebra , 1997 .

[9]  Rachid Deriche,et al.  Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI , 2014, IEEE Transactions on Medical Imaging.

[10]  Santiago Aja-Fernández,et al.  DWI filtering using joint information for DTI and HARDI , 2010, Medical Image Anal..

[11]  Stefan Skare,et al.  Ultra-high resolution diffusion tensor imaging of the microscopic pathways of the medial temporal lobe , 2012, NeuroImage.

[12]  Pierrick Coupé,et al.  Author manuscript, published in "Journal of Magnetic Resonance Imaging 2010;31(1):192-203" DOI: 10.1002/jmri.22003 Adaptive Non-Local Means Denoising of MR Images with Spatially Varying Noise Levels , 2010 .

[13]  Daniel C. Alexander,et al.  Camino: Open-Source Diffusion-MRI Reconstruction and Processing , 2006 .

[14]  Qianjin Feng,et al.  Denoising of 3 D Magnetic Resonance Images by Using 1 Higher-Order Singular Value Decomposition 2 , 2014 .

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  Truong Q. Nguyen,et al.  Novel Example-Based Method for Super-Resolution and Denoising of Medical Images , 2014, IEEE Transactions on Image Processing.

[17]  Simon K. Warfield,et al.  Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions , 2012, Medical Image Anal..

[18]  Stuart Crozier,et al.  Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images , 2012, NeuroImage.

[19]  Alan Connelly,et al.  MRtrix: Diffusion tractography in crossing fiber regions , 2012, Int. J. Imaging Syst. Technol..

[20]  Salah Bourennane,et al.  Adaptive Flattening for Multidimensional Image Restoration , 2008, IEEE Signal Processing Letters.

[21]  A. Albanese,et al.  White Matter Involvement in Idiopathic Parkinson Disease: A Diffusion Tensor Imaging Study , 2009, American Journal of Neuroradiology.

[22]  Xinyuan Zhang,et al.  Denoising of 3D magnetic resonance images by using higher-order singular value decomposition , 2015, Medical Image Anal..

[23]  Maxime Descoteaux,et al.  Collaborative patch-based super-resolution for diffusion-weighted images , 2013, NeuroImage.

[24]  S Peled,et al.  Superresolution in MRI: Application to human white matter fiber tract visualization by diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[25]  Norberto Malpica,et al.  Single-image super-resolution of brain MR images using overcomplete dictionaries , 2013, Medical Image Anal..

[26]  Demetri Terzopoulos,et al.  Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.