Reconstruction of High-Resolution Tongue Volumes From MRI

Magnetic resonance images of the tongue have been used in both clinical studies and scientific research to reveal tongue structure. In order to extract different features of the tongue and its relation to the vocal tract, it is beneficial to acquire three orthogonal image volumes-e.g., axial, sagittal, and coronal volumes. In order to maintain both low noise and high visual detail and minimize the blurred effect due to involuntary motion artifacts, each set of images is acquired with an in-plane resolution that is much better than the through-plane resolution. As a result, any one dataset, by itself, is not ideal for automatic volumetric analyses such as segmentation, registration, and atlas building or even for visualization when oblique slices are required. This paper presents a method of superresolution volume reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image volumes. The method uses preprocessing steps that include registration and intensity matching and a data combination approach with the edge-preserving property carried out by Markov random field optimization. The performance of the proposed method was demonstrated on 15 clinical datasets, preserving anatomical details and yielding superior results when compared with different reconstruction methods as visually and quantitatively assessed.

[1]  Ying Bai,et al.  Super-resolution reconstruction for tongue MR images , 2012, Medical Imaging.

[2]  Harry Shum,et al.  Face Hallucination: Theory and Practice , 2007, International Journal of Computer Vision.

[3]  V J Napadow,et al.  Intramural mechanics of the human tongue in association with physiological deformations. , 1999, Journal of biomechanics.

[4]  Michel Barlaud,et al.  Deterministic edge-preserving regularization in computed imaging , 1997, IEEE Trans. Image Process..

[5]  Nikolas P. Galatsanos,et al.  Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images , 2006, IEEE Transactions on Image Processing.

[6]  Liangpei Zhang,et al.  A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution , 2007, IEEE Transactions on Image Processing.

[7]  T. Hebert,et al.  A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors. , 1989, IEEE transactions on medical imaging.

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

[9]  N. Alon,et al.  Resolution enhancement in MRI. , 2006, Magnetic resonance imaging.

[10]  M J McCutcheon,et al.  MR imaging of the vocal tract during vowel production , 1991, Journal of magnetic resonance imaging : JMRI.

[11]  Jérôme Idier,et al.  Optimized single site update algorithms for image deblurring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[12]  Jerry L Prince,et al.  Super-resolution Reconstruction of MR Brain Images , 2004 .

[13]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[14]  Michel Barlaud,et al.  Two deterministic half-quadratic regularization algorithms for computed imaging , 1994, Proceedings of 1st International Conference on Image Processing.

[15]  Shrikanth S. Narayanan,et al.  An articulatory study of fricative consonants using magnetic resonance imaging , 1995 .

[16]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[17]  François Rousseau,et al.  Brain Hallucination , 2008, ECCV.

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

[19]  Jerry L. Prince,et al.  Motion of apical and laminal /s/ in normal and post-glossectomy speakers , 2012 .

[20]  A. N. Tikhonov,et al.  REGULARIZATION OF INCORRECTLY POSED PROBLEMS , 1963 .

[21]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Maureen Stone,et al.  A preliminary application of principal components and cluster analysis to internal tongue deformation patterns , 2010, Computer methods in biomechanics and biomedical engineering.

[23]  Robert L. Stevenson,et al.  Super-resolution from image sequences-a review , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

[24]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[25]  Yoon-Chul Kim,et al.  Seeing speech: Capturing vocal tract shaping using real-time magnetic resonance imaging [Exploratory DSP] , 2008, IEEE Signal Processing Magazine.

[26]  Daniel Rueckert,et al.  MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies , 2007, IEEE Transactions on Medical Imaging.

[27]  Hayit Greenspan,et al.  MRI Inter-slice Reconstruction Using Super-Resolution , 2001, MICCAI.

[28]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[29]  Shrikanth S. Narayanan,et al.  Geometry, kinematics, and acoustics of Tamil liquid consonants. , 1999, The Journal of the Acoustical Society of America.

[30]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[31]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[32]  Nikolas P. Galatsanos,et al.  Super-Resolution Based on Fast Registration and Maximum a Posteriori Reconstruction , 2007, IEEE Transactions on Image Processing.

[33]  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..

[34]  Colin Studholme,et al.  On Super-Resolution for Fetal Brain MRI , 2010, MICCAI.

[35]  Michal Irani,et al.  Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[36]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[37]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[38]  Hua Huang,et al.  Neighbor embedding based super-resolution algorithm through edge detection and feature selection , 2009, Pattern Recognit. Lett..

[39]  Nanning Zheng,et al.  Image hallucination with primal sketch priors , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[40]  M. Ng,et al.  Superresolution image reconstruction using fast inpainting algorithms , 2007 .

[41]  Kwang In Kim,et al.  Example-Based Learning for Single-Image Super-Resolution , 2008, DAGM-Symposium.

[42]  Wiro J Niessen,et al.  Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time? , 2012, Magnetic resonance in medicine.

[43]  Olivier D. Faugeras,et al.  Variational Methods for Multimodal Image Matching , 2002, International Journal of Computer Vision.

[44]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[45]  P. Green Bayesian reconstructions from emission tomography data using a modified EM algorithm. , 1990, IEEE transactions on medical imaging.

[46]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.

[47]  Russell M. Mersereau,et al.  A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI , 2009, IEEE Transactions on Medical Imaging.

[48]  Hayit Greenspan,et al.  Super-Resolution in Medical Imaging , 2009, Comput. J..

[49]  Simon K. Warfield,et al.  Super-Resolution in Diffusion-Weighted Imaging , 2011, MICCAI.

[50]  Simon K. Warfield,et al.  Maximum A Posteriori Estimation of Isotropic High-Resolution Volumetric MRI from Orthogonal Thick-Slice Scans , 2010, MICCAI.

[51]  Michael Elad,et al.  A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur , 2001, IEEE Trans. Image Process..

[52]  Joos Vandewalle,et al.  Super-Resolution From Unregistered and Totally Aliased Signals Using Subspace Methods , 2007, IEEE Transactions on Signal Processing.

[53]  W S Levine,et al.  Modeling tongue surface contours from Cine-MRI images. , 2001, Journal of speech, language, and hearing research : JSLHR.

[54]  R. Wilhelms-Tricarico Physiological modeling of speech production: methods for modeling soft-tissue articulators. , 1995, The Journal of the Acoustical Society of America.

[55]  Kiyoshi Honda,et al.  An MRI analysis of the extrinsic tongue muscles during vowel production , 2007, Speech Commun..

[56]  Edmund Y. Lam,et al.  A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video , 2007, EURASIP J. Adv. Signal Process..

[57]  Horst Bischof,et al.  A Convex Approach for Variational Super-Resolution , 2010, DAGM-Symposium.

[58]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[59]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[60]  Peyman Milanfar,et al.  A computationally efficient superresolution image reconstruction algorithm , 2001, IEEE Trans. Image Process..

[61]  Yves Goussard,et al.  Three-dimensional edge-preserving image enhancement for computed tomography , 2003, IEEE Transactions on Medical Imaging.

[62]  Colin Studholme,et al.  Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. , 2006, Academic radiology.

[63]  Jan Sijbers,et al.  General and Efficient Super-Resolution Method for Multi-slice MRI , 2010, MICCAI.