![]() ![]() Previous attempts on MRI denoising can be categorized in three different ways: traditional methods, supervised learning, and unsupervised learning. Therefore, we will compare two unsupervised denoising approaches that denoise MRI in the spatial frequency space, competing with the more classical and widely used denoising methods. We wanted to explore an unsupervised approach using the complex image space, where no ground truth data is needed. Most of the MRI denoising methods available use a supervised approach where they use the original MRI as ground truth. Additionally, many traditional techniques denoise MRI in the magnitude space, dismissing the innate spatial frequency information the MRI contain. While deep learning has seen success in many areas, there is a lack of methods focused on denoising MRI. Self-supervised denoisers generally under-perform supervised techniques, but arise naturally in cases like MRI, where pure supervised learning is infeasible. Deep self-supervised image denoisers have been seeing recent success for general image denoising tasks, and provide robust denoisers without requiring access to denoised images. As such, we evaluate self-supervised solutions to MRI denoising. Thus either synthetic data needs be generated for supervised learning or unsupervised and self-supervised strategies must be employed. Likewise, due to previously discussed movement of the subject, two independent samples for denoising strategies as used by Lehtinen et al. ![]() Thus, when training a MRI denoiser, no ground truth is available for the training procedure. Finally, the patient’s body temperature and the thermal factor from the MR machine is another key element, specially since a long exposure inside the MR machine could lead to an increase in body temperature, web (2017). Another source of thermal noise is inversely proportional to the amount of time that the subject stays inside the MR machine, and while in the machine the subjects movements also contribute to the thermal noise. All other factors withheld, the MR machine has an innate noise component when acquiring an image due to a thermal factor. When taking an MRI of a living subject, there are multiple noise factors. Thus, there is a necessity for an efficient MRI reconstruction process, where denoising methods are applied to noisy images in order to improve both qualitative and quantitative measures of MRI.Īdditionally, in the case of in vivo MRI, noise is implicit to the acquisition process. The utility of MRI decreases if a region or specific tissue suffers from a low signal to noise ratio. In addition to visually corrupting the recovered images, noise is also an obstacle when conducting quantitative imaging on the MRI. Noise in MRI is of major consequence as it can mislead and result in inaccurate diagnoses of patients. In MRI, data needed to generate images is directly sampled from the spatial frequency domain however, the quality of this data can be deteriorated by several thermal noise sources and artifacts. Magnetic Resonance Imaging, MRI, is one of the most widely used imaging techniques, as it provides detailed information about organs and tissues in a completely non-invasive way. For most given metrics and qualitative measures, both networks outperformed NLM, and they prove to be reliable denoising methods. ![]() ![]() Both networks are compared against a state-of-the-art algorithm, Non-Local Means (NLM) using quantitative and qualitative measures. These datasets contain information about the complex image space which will be used for denoising purposes. Both methods are tested on two different datasets, one containing real knee MRI and the other consists of synthetic brain MRI. The first method is based on Stein’s Unbiased Risk Estimator, while the second approach is based on a blindspot network, which limits the network’s receptive field. In this paper we evaluate two unsupervised approaches to denoise Magnetic Resonance Images (MRI) in the complex image space using the raw information that k-space holds. 2Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA, United States.1Department of Computer Science, University of Colorado Colorado Springs, Colorado Springs, CO, United States. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |