Deep learning enables quantitative kinetic modeling from low-dose dynamic [$$^{18}$$F]-MK6240 Tau PET

Dynamic positron emission tomography (PET) imaging with the tau tracer [\(^\)F]-MK6240 is widely used in Alzheimer’s disease research, but the time frames of a dynamic acquisition are short and contain few detected events. This makes the images noisy, and leads to inaccurate estimates of kinetic parameters, especially when estimating kinetic parameters at the voxel level. Reducing the injected dose would lower radiation exposure and make repeated scanning more practical, but only if kinetic accuracy is preserved.

In this work we evaluated two deep learning denoising methods, U-Net and Restormer, on low-dose (10% of full dose) dynamic [\(^\)F]-MK6240 PET data from 59 subjects, covering cognitively normal individuals and patients with mild cognitive impairment and Alzheimer’s disease. We assessed performance through time-activity curve fidelity, parametric map quality, and regional bias and variance analysis for two clinically critical biomarkers, namely the relative tracer delivery rate R1 and the distribution volume ratio DVR. Both methods clearly reduced bias and standard deviation of kinetic parameters compared to unprocessed low-dose images.

Restormer showed lower bias than U-Net across most brain regions and time frames, with better preserved TAC shape particularly in the late frames critical for DVR estimation. The results support that a 90% dose reduction is possible without losing the quantitative accuracy needed for tau burden assessment in clinical and research settings. The authors thank the clinical staff at Massachusetts General Hospital for subject recruitment and data acquisition support.

This work was supported in part by the National Institutes of Health under Grants P41EB022544, R01EB035093 and R01AG076153. Yale Biomedical Imaging Institute and Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA Se-In Jang, Yassir Najmaoui, Yanis Chemli, Nicolas Guehl, Maeva Dhaynaut, Georges El Fakhri, Chao Ma, Jinsong Ouyang & Thibault Marin School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL, USA Correspondence to Jinsong Ouyang or Thibault Marin. The authors declare no competing interests.

This work involved human subjects. Ethics approval was granted by the Institutional Review Board at Massachusetts General Hospital (Protocol 2021P003519) and all procedures were performed in accordance with the Declaration of Helsinki. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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