Longitudinal Guided Super-Resolution Reconstruction of Neonatal Brain MR Images - Longitudinal Registration and Shape Modeling - page 70

generally have lower tissue contrast in neonates. We also tried other strategies such as
using longitudinal T2 images as prior for neonatal T1 image reconstruction, or using
longitudinal T1 images as prior for neonatal T2 image reconstruction. These results
are similar to those obtained with the use of priors from the same modality (p>0.05).

4 Conclusion

We have presented a super-resolution method that combines both self-similar infor-
mation within an image and the longitudinal information from its follow-up image.
High-resolution reconstruction results of neonatal images indicate that the proposed
method outperforms methods such as NN interpolation, spline-based interpolation,
NLM upsampling, and LRTV-based super-resolution. Currently, the proposed method
only works on images with longitudinal follow-ups. In future, we anticipate using the
results as training data to learn their longitudinal developmental constraints and thus
extend the method on single time-point images.

References

1. Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical
overview. IEEE Signal Processing Magazine 20 , 21-36 (2003)
2. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Com-
puter Graphics and Applications 22 , 56-65 (2002)
3. Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annu.
Rev. Biomed. Eng. 2 , 315-337 (2000)
4. Lehmann, T.M., Gonner, C., Spitzer, K.: Survey: interpolation methods in medical image
processing. IEEE Trans. Med. Imaging 18 , 1049-1075 (1999)
5. Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Advances and challenges in super-
resolution. International Journal of Imaging Systems and Technology 14 , 47-57 (2004)
6. Manjón, J.V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., Robles, M.: Non-local
MRI upsampling. Medical Image Analysis 14 , 784-792 (2010)
7. Shi, F., Cheng, J., Wang, L., Yap, P.-T., Shen, D.: Low-Rank Total Variation for Image
Super-Resolution. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.)
MICCAI 2013, Part I. LNCS, vol. 8149, pp. 155-162. Springer, Heidelberg (2013)
8. Shi, F., Fan, Y., Tang, S., Gilmore, J.H., Lin, W., Shen, D.: Neonatal brain image segmen-
tation in longitudinal MRI studies. Neuroimage 49 , 391-400 (2010)
9. Armstrong, E., Schleicher, A., Omran, H., Curtis, M., Zilles, K.: The Ontogeny of Human
Gyrification. Cerebral Cortex 5 , 56-63 (1995)
10. Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.
2, pp. 60-65 (2005)
11. Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor Completion for Estimating Missing
Values in Visual Data. IEEE Transactions on Pattern Analysis and Machine Intelligence
35 , 208-220 (2013)
12. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algo-
rithms. Physica D: Nonlinear Phenomena 60 , 259-268 (1992)