Self-supervised denoising for medical imaging enhancement

Abstract

Self-supervised denoising has emerged as a promising approach for enhancing the quality of medical imaging, particularly in modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and optical microscopy. Traditional supervised methods often require large datasets of paired noisy and clean images, which are challenging to acquire in clinical practice. In contrast, self-supervised strategies exploit the inherent redundancy and structure within the data itself, enabling effective noise reduction without the need for explicitly labeled training pairs. This Perspective synthesizes recent advances in self-supervised denoising techniques, outlining their underlying principles, algorithmic innovations, and practical applications across different imaging modalities. In MRI, these methods have been shown to improve contrast and detail resolution, while in CT, they contribute to reducing radiation dose by allowing lower signal acquisitions without compromising image quality. In optical microscopy, self-supervised denoising facilitates extracting high-fidelity cellular information from inherently low-light environments. Furthermore, these techniques have also proven effective in imaging of biomedical materials, such as tissue engineering scaffolds, drug delivery systems, and implants, improving the evaluation of their interactions with biological tissues. Collectively, the integration of these advanced denoising frameworks holds significant promise for improving diagnostic accuracy, streamlining clinical workflows, and ultimately enhancing patient outcomes.

Keywords

clinical imaging / computational image analysis / deep learning / optical microscopy / self-supervised denoising

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