access icon free 3D MRI image super-resolution for brain combining rigid and large diffeomorphic registration

Most of the recent leading multiple magnetic resonance imaging (MRI) super-resolution techniques for brain are limited to rigid motion. In this study, the authors aim to develop a super-resolution technique with diffeomorphism mainly for longitudinal brain MRI data. For the images from different time slots, unpredicted deformation may occur. In previous studies, sole rigid registration or traditional non-rigid registration has been frequently used to achieve multi-plane super-resolution. However, non-rigid motion of two brains from different time slots is difficult to model, since brain contains a wealth of complex structure such as the cerebral cortex. In order to address such problem, rigid and large diffeomorphic registration has been embedded into their super-resolution framework. In addition, many previous researchers use norm to achieve super-resolution framework. In this work, norm minimisation and regularisation based on a bilateral prior are adopted. These operations ensure its robustness to the assumed model of data and noise. Their approach is evaluated using Alzheimer datasets from seven different resolutions. Results show that their reconstructions have advantages over rigid and conventional non-rigid registration-based super-resolution, in terms of the root-mean-square error and structure similarity. Furthermore, their reconstruction results improve the precision of brain automatic segmentation.

Inspec keywords: diseases; image registration; medical image processing; image resolution; brain; biomedical MRI

Other keywords: unpredicted deformation; Alzheimer; magnetic resonance imaging super-resolution techniques; rigid registration; complex structure; brain neuropathy; large diffeomorphic registration; cerebral cortex; 3D MRI image super-resolution

Subjects: Biology and medical computing; Medical magnetic resonance imaging and spectroscopy; Biophysics of neurophysiological processes; Optical, image and video signal processing; Computer vision and image processing techniques; Biomedical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation

References

    1. 1)
      • 35. Yuan, Q.Q., Zhang, L.P., Shen, H.F.: ‘Multi frame super-resolution employing a spatially weighted total variation model’, IEEE Trans. Circ. Syst. Video Technol., 2012, 22, (3), pp. 379392.
    2. 2)
      • 41. Pier, D.B., Gholipour, A., Afacan, O., et al: ‘3D super-resolution motion-corrected MRI: validation of fetal posterior fossa measurements’, J. Neuroimag., 2016, 26, pp. 539544.
    3. 3)
      • 15. Shilling, R.Z., Robbie, T., Bailloeul, T., et al: ‘A super-resolution framework for 3-D high-resolution and high-contrast imaging using 2-D multislice MRI’, IEEE Trans. Med. Imaging, 2009, 28, (5), pp. 633644.
    4. 4)
      • 16. Yim, P.J., Maros, H.B., McAuliffe, M., et al: ‘Registration of time-series contrast enhanced magnetic resonance images for renography’. IEEE Symposium on Computer-Based Medical Systems, Bethesda, MD, USA, July 2001, pp. 516520.
    5. 5)
      • 48. Avants, B.B., Tustison, N.J., Song, G., et al: ‘A reproducible evaluation of ANTs similarity metric performance in brain image registration’, NeuroImage, 2011, 54, (3), pp. 20332044.
    6. 6)
      • 30. Patti, A.J., Altunbasak, Y.: ‘Artifact reduction for POCS-based super resolution with edge adaptive regularization and higher-order interpolants’. 1998 Int. Conf. Image Processing – Proc., 1998, vol. 3, pp. 217221.
    7. 7)
      • 33. Farsiu, S., Robinson, M.D., Elad, M., et al: ‘Fast and robust multiframe super resolution’, IEEE Trans. Image Process, 2004, 13, (10), pp. 13271344.
    8. 8)
      • 32. Yue, L., Shen, N., Yuan, Q., et al: ‘A locally adaptive L1-L2 norm for multi-frame super-resolution of images with mixed noise and outliers’, Signal Process., 2014, 105, pp. 156174.
    9. 9)
      • 44. Ropele, S., Ebner, F., Fazekas, F., et al: ‘Super-resolution MRI using microscopic spatial modulation of magnetization’, Magnet. Reson. Med., 2010, 64, (6), pp. 16711675.
    10. 10)
      • 3. Van Reeth, E., Tham, I., Tan, C., et al: ‘Super-resolution in magnetic resonance imaging: a review’, Concepts Magn. Reson. A, 2012, 40A, (6), pp. 306325.
    11. 11)
      • 49. Avants, B.B., Epstein, C.L., Grossman, M., et al: ‘Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain’, Med. Image Anal., 2008, 12, (1), pp. 2641.
    12. 12)
      • 52. Gray, K.R., Aljabar, P, Heckemann, R.A., et al: ‘Random forest-based similarity measures for multi-modal classification of Alzheimer's disease’, Neuroimage, 2013, 65, pp. 167175.
    13. 13)
      • 21. Rousseau, F., Glenn, O.A., Iordanova, B., et al: ‘Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images’, Acad. Radiol., 2006, 13, (9), pp. 10721081.
    14. 14)
      • 6. Shilling, R.Z., Ramamurthy, S., Brummer, M.E.: ‘Sampling strategies for super-resolution in multi-slice MRI’, IEEE International Conference on Image Processing, San Diego, CA, USA, 2008, pp. 22402243.
    15. 15)
      • 29. Fan, C., Zhu, J., Gong, J., et al: ‘POCS super-resolution sequence image reconstruction based on improvement approach of Keren registration method’. ISDA 2006: Sixth Int. Conf. Intelligent Systems Design and Applications, 2006, vol. 2, pp. 333337.
    16. 16)
      • 17. Pataky, T.C., Goulermas, J.Y., Crompton, R.H.: ‘A comparison of seven methods of within-subjects rigid-body pedobarographic image registration’, J. Biomech., 2008, 41, (14), pp. 30853089.
    17. 17)
      • 8. Carmi, E., Liu, S., Liu, S.Y., et al: ‘Resolution enhancement in MRI’, Magn. Reson. Imaging, 2006, 24, (2), pp. 133154.
    18. 18)
      • 19. Moghari, M.H., Abolmaesumi, P.: ‘Point-based rigid-body registration using an unscented Kalman filter’, IEEE Trans. Med. Imaging, 2007, 26, (12), pp. 17081728.
    19. 19)
      • 12. Misu, T., Matsuo, Y., Sakaida, S., et al: ‘Motion-adaptive sub-Nyquist sampling technique for multi-frame super-resolution’. 2012 Picture Coding Symp. (PCS), 2012, pp. 321324.
    20. 20)
      • 11. Prabhu, S.M., Rajagopalan, A.N.: ‘Joint multi-frame super-resolution and matting’. Proceedings of the 21st International Conference on Pattern Recognition, Tsukuba, Japan, November 1999, pp. 19241927.
    21. 21)
      • 38. Wang, Y., Qiao, J., Li J, et al: ‘Sparse representation-based MRI super-resolution reconstruction’, Measurement, 2014, 47, pp. 946953.
    22. 22)
      • 7. Greenspan, H., Oz, G., Kiryati, N., et al: ‘MRI inter-slice reconstruction using super-resolution’, Magn. Reson. Imaging, 2002, 20, (5), pp. 437446.
    23. 23)
      • 43. Mudenagudi, U., Banerjee, S., Kalra, P.K.: ‘Space-time super-resolution using graph-cut optimization’, IEEE Trans. Pattern Anal., 2011, 33, (5), pp. 9951008.
    24. 24)
      • 31. Luo, Z., Wu, J.H.: ‘A POCS super-resolution image reconstruction based on the projection residue’, Proc. SPIE, 2012, 8349.
    25. 25)
      • 23. Rahman, S.u., Wesarg, S.: ‘Combining short-axis and long-axis cardiac MR images by applying a super-resolution reconstruction algorithm’. Medical Imaging 2010: Image Processing, 2010, p. 76230I.
    26. 26)
      • 54. Garcia-Vazquez, V., Reig, S., Janssen, J., et al: ‘Use of IBASPM atlas-based automatic segmentation toolbox in pathological brains: effect of template selection’. 2008 IEEE Nuclear Science Symp. and Medical Imaging Conf. (2008 NSS/MIC), 2009, vol. 1–9, pp. 35443546.
    27. 27)
      • 9. Qin, F.Q., He, X., Chen, W., et al: ‘Video superresolution reconstruction based on subpixel registration and iterative back projection’, J. Electron. Imaging, 2009, 18, (1).
    28. 28)
      • 20. Shi, F., Cheng, J., Wang, L., et al: ‘Longitudinal guided super-resolution reconstruction of neonatal brain MR images’. Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, 2015, vol. 8682, pp. 6776.
    29. 29)
      • 53. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600612.
    30. 30)
      • 55. Djamanakova A., Faria, A.V., Ceritoglu C, , et al: ‘Diffeomorphic brain mapping based on T1-weighted images: improvement of registration accuracy by multichannel mapping’, J. Magn. Reson. Imag., 2013, 37, (1), pp. 7684.
    31. 31)
      • 14. Tian, Y.S., Yap, K.H.: ‘Multi-frame super-resolution from observations with zooming motion’. 2012 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 12571260.
    32. 32)
      • 24. Chilla, G.S., Tan, C.H., Poh, C.L.: ‘Deformable registration based super-resolution for isotropic reconstruction of 4D MRI volumes’, IEEE J. Biomed. Health Inform., 2017, 99, p. 00619.
    33. 33)
      • 37. Laghrib, A., Hakim, A., Raghay, S.: ‘A combined total variation and bilateral filter approach for image robust super resolution’, EURASIP J. Image Video Process., 2015, 2015, (1), pp. 110.
    34. 34)
      • 45. Rousseau, F., Kim, K., Studholme, C., et al: ‘On super-resolution for fetal brain MRI’. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Pt II, 2010, vol. 6362, pp. 355362.
    35. 35)
      • 42. Odille, F., Bustin, A., Chen, B., et al: ‘Motion-corrected, super-resolution reconstruction for high-resolution 3D cardiac cine MRI’. MICCAI 2015, Part III, 2015 (LNCS, 9351), pp. 435442.
    36. 36)
      • 5. Kornprobst, P., Nikolova, M., Peeters, R.R., et al: ‘The use of super-resolution techniques to reduce slice thickness in functional MRI’, Int. J. Imaging Syst. Tech., 2004, 14, (3), pp. 131138.
    37. 37)
      • 2. Fiat, D.: ‘Method of enhancing an MRI signal’. Number US Patent 6,294,914, 1997.
    38. 38)
      • 47. Bakircioglu, M.M., Joshi, S., Miller, M.I.: ‘Landmark matching on brain surfaces via large deformation diffeomorphisms on the sphere’. Medical Imaging 1999: Image Processing, Pts 1 And 2, 1999, vol. 3661, pp. 710715.
    39. 39)
      • 50. Cuingnet, R., Gerardin, E., Tessieras, J., et al: ‘Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database’, Neuroimage, 2011, 56, pp. 766781.
    40. 40)
      • 10. Kanaev, A.V.: ‘Confidence measures of optical flow estimation suitable for multi-frame super-resolution’. Visual Information Processing Xxi, 2012, vol. 8399.
    41. 41)
      • 27. Tang, X.Y., Yoshida, S., Hsu, J., et al: ‘Multi-contrast multi-atlas parcellation of diffusion tensor imaging of the human brain’, PLoS One, 2014, 9, (5), p. e96985.
    42. 42)
      • 22. Woo, J., Murano, E., Stone, M., et al: ‘Reconstruction of high-resolution tongue volumes from MRI’, IEEE Trans. Bio-Med. Eng., 2012, 59, (12), pp. 35113524.
    43. 43)
      • 36. Paul, R.: ‘Total variation regularization algorithms for images corrupted with different noise models: a review’, J. Electric. Comput. Eng., 2013, ID217021, pp. 118.
    44. 44)
      • 28. Liang, Z., He, X., Ceritoglu, C., et al: ‘Evaluation of cross-protocol stability of a fully automated brain multi-atlas parcellation tool’, PLoS One, 2015, 10, (7), p. e0133533.
    45. 45)
      • 1. Plenge, E., Poot, D.H., Bernsen, M., et al: ‘Super-resolution methods in MRI: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?’, Magnet. Reson. Med., 2012, 68, (6), pp. 19831993.
    46. 46)
      • 46. Tustison, N.J., Cook, P.A., Klein, A., et al: ‘Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements’, NeuroImage, 2014, 99, pp. 166179.
    47. 47)
      • 39. Rueda, A., Malpica, N., Romero, E.: ‘Single-image super-resolution of brain MR images using overcomplete dictionaries’, Med. Image Anal., 2013, 17, pp. 113132.
    48. 48)
      • 13. Farsiu, S., Elad, D., Milanfar, P.: ‘Multi-frame demosaicing and super-resolution from under-sampled color images’, Proc. Soc. Photo-Opt. Ins., 2004, 5299, pp. 222233.
    49. 49)
      • 26. Ashburner, J.: ‘A fast diffeomorphic image registration algorithm’, NeuroImage, 2007, 38, (1), pp. 95113.
    50. 50)
      • 25. Beg, M.F., Miller, M., Trouve, A., et al: ‘Computing large deformation metric mappings via geodesic flows of diffeomorphisms’, Int. J. Comput. Vis., 2005, 61, (2), pp. 139157.
    51. 51)
      • 18. Rajapakse, C.S., Magland, J., Wehrli, S.L., et al: ‘Efficient 3D rigid-body registration of micro-MR and micro-CT trabecular bone images – art. no. 69142z’. Medical Imaging 2008: Image Processing, Pts 1–3, 2008, vol. 6914, Z9142–Z9142.
    52. 52)
      • 34. Zhang, X., Lam, E.Y., Wu, E.X., et al: ‘Application of Tikhonov regularization to super-resolution reconstruction of brain MRI images’. Medical Imaging and Informatics, 2nd International Conference, Beijing China, August 2007, pp. 5156.
    53. 53)
      • 40. Jain, S., Sima, D.M., Sanaei, N.F., et al: ‘Patch-based super-resolution of MR spectroscopic images: application to multiple sclerosis’, Front. Neurosci., 2017, 11, p. 13, doi:10.3389/fnins.2017.00013.
    54. 54)
      • 51. Eskildsen, S.F., Coupe, P., Garcia-Lorenzo, D., et al: ‘Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning’, Neuroimage, 2013, 65, pp. 511521.
    55. 55)
      • 4. Peled, S., Yeshurun, Y.: ‘Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging’, Magnet. Reson. Med., 2001, 45, (1), pp. 2935.
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