Abstract
Spatial resolution, signal-to-noise ratio (SNR) and acquisition time are interconnected in magnetic resonance imaging (MRI). Trade-offs are made to keep the SNR at the acceptable level, maximizing the resolution, minimizing the acquisition time and maintaining radiologically useful images. In low-field MRI scanners and especially in diffusion imaging, these trade-offs are even more crucial due to a generally lower image quality. Image post-processing is necessary in such cases to improve image quality. In this work, we alleviate the challenges of low SNR in dMRI at low magnetic fields by performing super-resolution reconstruction (SRR). Our approach combines multiple low-resolution images acquired at different image slice rotations and employs a convolutional neural network to perform the SRR. Training is performed on noisy images. The network learns to extract and compose complementary image details into a super-resolution output image. Because of the properties of noise and the training process, the super-resolution images are less noisy than the directly acquired high-resolution ones, contain more high-resolution details than the input low-resolution images and the total acquisition time is decreased.
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
de Leeuw den Bouter, M.L., Ippolito, G., O’Reilly, T.P.A., Remis, R.F., van Gijzen, M.B., Webb, A.G.: Deep learning-based single image super-resolution for low-field MR brain images. Sci Rep 12(1) (2022). https://doi.org/10.1038/s41598-022-10298-6
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.2439281
Gu, J., Li, Z., Wang, Y., Yang, H., Qiao, Z., Yu, J.: Deep generative adversarial networks for thin-section infant MR image reconstruction. IEEE Access 7, 68290–68304 (2019). https://doi.org/10.1109/access.2019.2918926
Gudbjartsson, H., Patz, S.: The Rician distribution of noisy MRI data. Magn. Reson. Med. 34, 910–914 (1995). https://doi.org/10.1002/mrm.1910340618
Iglesias, J.E., Schleicher, R., Laguna, S., Billot, B., Schaefer, P., McKaig, B., Goldstein, J.N., Sheth, K.N., Rosen, M.S., Kimberly, W.T.: Accurate Super-Resolution Low-Field Brain MRI (2022). https://doi.org/10.48550/ARXIV.2202.03564
Jurek, J.: Super-Resolution Reconstruction of Three-dimensional Magnetic Resonance Images Using Deep and Transfer Learning (2020). https://doi.org/10.13140/RG.2.2.23612.46723
Jurek, J., Kociński, M., Materka, A., Elgalal, M., Majos, A.: CNN-based superresolution reconstruction of 3D MR images using thick-slice scans. Biocybern. Biomed. Eng. 40(1), 111–125 (2020). https://doi.org/10.1016/j.bbe.2019.10.003
Jurek, J., Materka, A., Ludwisiak, K., Majos, A., Gorczewski, K., Cepuch, K., Zawadzka, A.: Supervised denoising of diffusion-weighted magnetic resonance images using a convolutional neural network and transfer learning. Biocybern. Biomed. Eng. 43(1), 206–232 (2023). https://doi.org/10.1016/j.bbe.2022.12.006
Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., Aila, T.: Noise2noise: learning image restoration without clean data. In: Dy, J.G., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden. Proceedings of Machine Learning Research, vol. 80, pp. 2971–2980. PMLR (2018). http://proceedings.mlr.press/v80/lehtinen18a.html
Peled, S., Yeshurun, Y.: Superresolution in MRI: Application to human white matter fiber tract visualization by diffusion tensor imaging. Magn. Reson. Med. 45(1), 29–35 (2001). https://doi.org/10.1002/1522-2594(200101)45:1<29::aid-mrm1005>3.0.co;2-z
Reeth, E.V., Tham, I.W.K., Tan, C.H., Poh, C.L.: Super-resolution in magnetic resonance imaging: a review. Concepts Magn Reson Part 40A(6), 306–325 (2012). https://doi.org/10.1002/cmr.a.21249
Tax, C.M., Bastiani, M., Veraart, J., Garyfallidis, E., Irfanoglu, M.O.: What’s new and what’s next in diffusion MRI preprocessing. NeuroImage 249, 118830 (2022). https://doi.org/10.1016/j.neuroimage.2021.118830
Vis, G., Nilsson, M., Westin, C.F., Szczepankiewicz, F.: Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution. NeuroImage 245, 118673 (2021). https://doi.org/10.1016/j.neuroimage.2021.118673
Woo, J., Murano, E.Z., Stone, M., Prince, J.L.: Reconstruction of high-resolution tongue volumes from MRI. IEEE Trans. Biomed. Eng. 59(12), 3511–3524 (2012). https://doi.org/10.1109/tbme.2012.2218246
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jurek, J., Ludwisiak, K., Materka, A., Szczepankiewicz, F. (2024). Improving the Resolution and SNR of Diffusion Magnetic Resonance Images From a Low-Field Scanner. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-38430-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38429-5
Online ISBN: 978-3-031-38430-1
eBook Packages: EngineeringEngineering (R0)