University of Twente Student Theses
Impact of image post-processing on signal-to-noise ratio in 19F MRI
Eijmers, V. and Have, T.A. van der and Persaud, J.J. and Prins, S.C.A. (2025) Impact of image post-processing on signal-to-noise ratio in 19F MRI.
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| Abstract: | Fluorine-19 MRI (19F MRI) is an emerging non-invasive imaging modality that offers high specificity and contrast-to-noise ratio (CNR), making it highly suitable for visualizing the tumour microenvironment (TME). However, its clinical and preclinical applications are limited by inherently low sensitivity, resulting in poor signal-to-noise ratio (SNR). This study investigates the impact of various post-processing techniques - specifically denoising and super-resolution - on the 19F image quality. The goal was to enhance both SNR and CNR while preserving structural integrity, as measured by the structural similarity index (SSIM). A range of classical image processing methods and deep learning-based approaches were implemented and tested in multiple combinations on phantom datasets. The top three performing post-processing pipelines, identified based on quantitative image quality metrics, were further validated in vivo using mouse models. The result showed that applying Block Matching and 3D filtering (BM3D) was the most effective way to enhance SNR and CNR while maintaining acceptable SSIM. Additionally, this study found promising results in deep learning models specifically trained for this data. These results demonstrate clear improvements in image clarity and quality, suggesting that tailored post-processing can significantly enhance the utility of 19F MRI in both research and potentially clinical applications. |
| Item Type: | Student Thesis (Bachelor) |
| Clients: | Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, Amsterdam, Nederland |
| Faculty: | TNW: Science and Technology |
| Subject: | 44 medicine |
| Programme: | Technical Medicine BSc (50033) |
| Link to this item: | https://purl.utwente.nl/essays/106652 |
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