Abstract
Unavailability of high field magnetic resonance (MR) scanners has encouraged the development of post-processing algorithms to reconstruct 7T-like MR images from the given 3T MR images. Such algorithms have recently been developed with promising performance, but with the expensive requirement of paired 3T-7T MR images. Due to practical unsuitability to acquire example paired 3T-7T images in the real scenario, in this paper we explore the potential of the single image-based approach to reconstruct the 7T-like MR images. The intensity transformation between 3 T and 7T MR images is assumed to be linear and is initialized as Toeplitz form of Gaussian kernel in alternative minimization framework to reconstruct 7T-like images from 3T MR images. Further, to achieve the desired contrast we compute the relation between statistics of the intensity values of two different tissues for each of the original 3T & 7T and registered 3T MR image. This relation is used to constrain the solution space for 7T-like MR image reconstruction. The proposed algorithm provides comparable performance to the existing algorithms which require 3T-7T images pair. The qualitative and quantitative analysis is done for reconstruction and segmentation results, indicating the advantages of proposed work.
This work is financially supported by MietY, India.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release
Bahrami, K., Shi, F., Zong, X., Shin, H.W., An, H., Shen, D.: Reconstruction of 7T-Like images from 3T MRI. IEEE Trans. Med. Imaging 35(9), 2085–2097 (2016). https://doi.org/10.1109/TMI.2016.2549918
Bahrami, K., Rekik, I., Shi, F., Shen, D.: Joint reconstruction and segmentation of 7T-like MR Images from 3T MRI based on cascaded convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 764–772. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_87
Bahrami, K., Shi, F., Rekik, I., Shen, D.: Convolutional neural network for reconstruction of 7T-like Images from 3T MRI using appearance and anatomical features. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 39–47. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_5
Forstmann, B.U., Isaacs, B.R., Temel, Y.: Ultra high field MRI-guided deep brain stimulation. Trends Biotechnol. 35(10), 904–907 (2017). https://doi.org/10.1016/j.tibtech.2017.06.010
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001). https://doi.org/10.1016/S1361-8415(01)00036-6. http://www.sciencedirect.com/science/article/pii/S1361841501000366
Kaur, P., Sharma, A., Nigam, A., Bhavsar, A.: MR-SRNET: Transformation of low field MR images to high field MR images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), October 2018, pp. 2057–2061 (2018). https://doi.org/10.1109/ICIP.2018.8451021
Zhang, Y., Cheng, J.-Z., Xiang, L., Yap, P.-T., Shen, D.: Dual-domain cascaded regression for synthesizing 7T from 3T MRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 410–417. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_47
van der Zwaag, W., Schäfer, A., Marques, J.P., Turner, R., Trampel, R.: Recent applications of UHF-MRI in the study of human brain function and structure: a review. NMR Biomed. 29(9), 1274–1288. https://doi.org/10.1002/nbm.3275. https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.3275
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kaur, P., Sao, A.K. (2019). Single Image Based Reconstruction of High Field-Like MR Images. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-32248-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32247-2
Online ISBN: 978-3-030-32248-9
eBook Packages: Computer ScienceComputer Science (R0)
