Preclinical comparative study of [18F]AlF-PSMA-11 and [18F]PSMA-1007 in varying PSMA expressing tumors

Piron, Sarah; Verhoeven, Jeroen; Courtyn, Jan; Kersemans, Ken; Descamps, Benedicte; Pieters, Leen; Vral, Anne; Vanhove, Christian; Vos, Filip De

doi:10.1038/s41598-022-20060-7

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…The most common PET tracer is uorodeoxyglucose (FDG) labelled with uorine-18 ( 18 F) to image glucose metabolism [1]. Other emerging tracers with more speci c uptakes are radiolabeled prostate-speci c membrane antigen (PSMA) [2] or broblast activation protein inhibitor (FAPI) [3] ligands. Preclinical PET imaging serves as a translational tool in tracer development.…”

Section: Introductionmentioning

confidence: 99%

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

Muller,

Vervenne,

Maebe

et al. 2023

Mol Imaging Biol

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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 imagebased deep learning (DL) framework for denoising lower count micro-PET images. Procedures:For 36 mice, a 15-min 18 F-FDG (8.15 ± 1.34 MBq) PET scan was acquired at 40 min post-injection on the Molecubes b-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 mins 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 lters; 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 ( 18 F-PSMA, 18 F-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 tremendous exibility in optimizing scan protocols with reduced tracer doses or shorter durations.

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Section: Introductionmentioning

confidence: 99%

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

Muller,

Vervenne,

Maebe

et al. 2023

Mol Imaging Biol

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

Muller

Vervenne

Maebe

et al. 2023

Preprint

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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 b-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 mins 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 68Ga-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 tremendous flexibility in optimizing scan protocols with reduced tracer doses or shorter durations.

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Preclinical comparative study of [18F]AlF-PSMA-11 and [18F]PSMA-1007 in varying PSMA expressing tumors

Cited by 2 publications

References 40 publications

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

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

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

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