Abstract
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. 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. Both methods are tested on two different datasets, one containing real knee MRI and the other consists of synthetic brain MRI. These datasets contain information about the complex image space which will be used for denoising purposes. Both networks are compared against a state-of-the-art algorithm, Non-Local Means (NLM) using quantitative and qualitative measures. For most given metrics and qualitative measures, both networks outperformed NLM, and they prove to be reliable denoising methods.
Highlights
Magnetic Resonance Imaging, Magnetic Resonance Images (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
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
We followed with twice the amount of noise with σ 2 × 10− 5 to test both algorithms with an elevated amount of noise
Summary
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. In MRI, data needed to generate images is directly sampled from the spatial frequency domain; the quality of this data can be deteriorated by several thermal noise sources and artifacts. K-space can be seen as an array of numbers representing spatial frequencies in the MRI. To transition between k-space and the complex image space, we apply an inverse fast Fourier transform, and vice versa. Even though they are visually different, the information contained in both spaces is the exactly the same.
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