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Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability

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Abstract

Purpose

Positron emission tomography (PET) image quality can be improved by higher injected activity and/or longer acquisition time, but both may often not be practical in preclinical imaging. Common preclinical radioactive doses (10 MBq) have been shown to cause deterministic changes in biological pathways. Reducing the injected tracer activity and/or shortening the scan time inevitably results in low-count acquisitions which poses a challenge because of the inherent noise introduction. We present an image-based deep learning (DL) framework for denoising lower count micro-PET images.

Procedures

For 36 mice, a 15-min [18F]FDG (8.15 ± 1.34 MBq) PET scan was acquired at 40 min post-injection on the Molecubes β-CUBE (in list mode). The 15-min acquisition (high-count) was parsed into smaller time fractions of 7.50, 3.75, 1.50, and 0.75 min to emulate images reconstructed at 50, 25, 10, and 5% of the full counts, respectively. A 2D U-Net was trained with mean-squared-error loss on 28 high-low count image pairs.

Results

The DL algorithms were visually and quantitatively compared to spatial and edge-preserving denoising filters; the DL-based methods effectively removed image noise and recovered image details much better while keeping quantitative (SUV) accuracy. The largest improvement in image quality was seen in the images reconstructed with 10 and 5% of the counts (equivalent to sub-1 MBq or sub-1 min mouse imaging). The DL-based denoising framework was also successfully applied on the NEMA-NU4 phantom and different tracer studies ([18F]PSMA, [18F]FAPI, and [68 Ga]FAPI).

Conclusion

Visual and quantitative results support the superior performance and robustness in image denoising of the implemented DL models for low statistics micro-PET. This offers much more flexibility in optimizing preclinical, longitudinal imaging protocols with reduced tracer doses or shorter durations.

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Acknowledgements

The authors would like to thank Dr. Pieter Mollet and Dr. Bert Vandeghinste (Molecubes) for their technical support with the β-CUBE and Dr. Elizabeth Li and Dr. Srilalan Krishnamoorthy for their suggestions on data visualization. The authors would like to acknowledge support from Small Animal Imaging Facility at UPenn.

Funding

This work was supported by FM’s UGent BOF doctoral grant. FM is financially supported by the Belgian American Educational Foundation and Fulbright Foreign Student Program.

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Authors and Affiliations

  1. Medical Image and Signal Processing (MEDISIP), Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000, Ghent, Belgium

    Florence M. Muller, Boris Vervenne, Jens Maebe, Christian Vanhove & Stefaan Vandenberghe

  2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA

    Florence M. Muller, Eric Blankemeyer, Mark A. Sellmyer, Rong Zhou & Joel S. Karp

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  1. Florence M. Muller
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Correspondence to Florence M. Muller.

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Florence M. Muller and Boris Vervenne are co-first authors.

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Muller, F.M., Vervenne, B., Maebe, J. et al. Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability. Mol Imaging Biol 26, 101–113 (2024). https://doi.org/10.1007/s11307-023-01866-x

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