Pfaff L, Darwish O, Wagner F, Thies M, Vysotskaya N, Hoßbach J, Weiland E, Benkert T, Eichner C, Nickel D, Wuerfl T, Maier A (2024)
Publication Type: Journal article
Publication year: 2024
Book Volume: 14
Article Number: 24292
Journal Issue: 1
DOI: 10.1038/s41598-024-75007-x
Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.
APA:
Pfaff, L., Darwish, O., Wagner, F., Thies, M., Vysotskaya, N., Hoßbach, J.,... Maier, A. (2024). Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-75007-x
MLA:
Pfaff, Laura, et al. "Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation." Scientific Reports 14.1 (2024).
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