Abstract
Diffusion magnetic resonance imaging (dMRI) is an important technique used in neuroimaging. It features a relatively low signal-to-noise ratio (SNR) which poses a challenge, especially at stronger diffusion weighting. A common solution to the resulting poor precision is to average signal from multiple identical measurements. Indeed, averaging the magnitude signal is sufficient if the noise is sampled from a distribution with zero mean value. However, at low SNR, the magnitude signal is increased by the rectified noise floor, such that the accuracy can only be maintained if averaging is performed on the complex signal. Averaging of the complex signal is straightforward in the non-diffusion-weighted images, however, in the presence of diffusion encoding gradients, any motion of the tissue will incur a phase shift in the signal which must be corrected prior to averaging. Instead, they are averaged in the modulus image space, which is associated with the effect of Rician bias. Moreover, repeated acquisitions further increase acquisition times which, in turn, exacerbate the challenges of patient motion. In this paper, we propose a method to correct phase variations using a neural network trained on synthetic MR data. Then, we train another network using the Noise2Noise paradigm to denoise real dMRI of the brain. We show that phase correction made Noise2Noise training possible and that the latter improved the denoising quality over averaging modulus domain images.
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References
Cocosco, C.A., Kollokian, V., Kwan, R.K.S., Pike, G.B., Evans, A.C.: BrainWeb: online interface to a 3D MRI simulated brain database. Neuroimage 5, 425 (1997)
Collins, D., et al.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17(3), 463–468 (1998). https://doi.org/10.1109/42.712135
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
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
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., et al.: 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
Kwan, R.K.S., Evans, A.C., Pike, G.B.: An extensible MRI simulator for post-processing evaluation. In: Hohne, K.H., Kikinis, R. (eds.) Visualization in Biomedical Computing. VBC 1996. Lecture Notes in Computer Science, vol. 1131. Springer, Heidelberg (1996). https://doi.org/10.1007/Bfb0046947
Kwan, R.S., Evans, A., Pike, G.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imaging 18, 1085–1097 (1999). https://doi.org/10.1109/42.816072
Lehtinen, J., et al.: 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, 10–15 July 2018. Proceedings of Machine Learning Research, vol. 80, pp. 2971–2980. PMLR (2018). https://proceedings.mlr.press/v80/lehtinen18a.html
Liu, F., et al.: Does perfect filtering really guarantee perfect phase correction for diffusion MRI data? Computer. Med. Imag. Graph. 103, 102160 (2023). https://doi.org/10.1016/j.compmedimag.2022.102160
Pizzolato, M., Gilbert, G., Thiran, J.P., Descoteaux, M., Deriche, R.: Adaptive phase correction of diffusion-weighted images. NeuroImage 206, 116274 (2020). https://doi.org/10.1016/j.neuroimage.2019.116274
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
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017). https://doi.org/10.1109/TIP.2017.2662206
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Kamil Gorczewski, Kamil Cepuch and Agata Zawadzka from Siemens Healthcare are warmly thanked for their assistance with MR image acquisition and comments on reconstruction.
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Jurek, J., Materka, A., Ludwisiak, K., Majos, A., Szczepankiewicz, F. (2023). Phase Correction and Noise-to-Noise Denoising of Diffusion Magnetic Resonance Images Using Neural Networks. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_61
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