Super-resolution reconstruction for tongue MR images

Magnetic resonance (MR) images of the tongue have been used in both clinical medicine and scientific research to reveal tongue structure and motion. In order to see different features of the tongue and its relation to the vocal tract it is beneficial to acquire three orthogonal image stacks-e.g., axial, sagittal and coronal volumes. In order to maintain both low noise and high visual detail, each set of images is typically acquired with in-plane resolution that is much better than the through-plane resolution. As a result, any one data set, by itself, is not ideal for automatic volumetric analyses such as segmentation and registration or even for visualization when oblique slices are required. This paper presents a method of super-resolution reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image stacks. The method uses preprocessing steps that include intensity matching and registration and a data combination approach carried out by Markov random field optimization. The performance of the proposed method was demonstrated on five clinical datasets, yielding superior results when compared with conventional reconstruction methods.

[1]  W S Levine,et al.  Modeling the motion of the internal tongue from tagged cine-MRI images. , 2001, The Journal of the Acoustical Society of America.

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

[3]  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.

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

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

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

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

[8]  Colin Studholme,et al.  A groupwise super-resolution approach: Application to brain MRI , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

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

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

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

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

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

[15]  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.

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

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

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

[19]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..