NIH grant: Systematic investigation of artifacts in deep learning-based PET image enhancement (1R03EB040241-01)

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NIH grant 1R03EB040241-01: Systematic investigation of artifacts in deep learning-based PET image enhancement
Institute: {'code': 'EB', 'abbreviation': 'NIBIB', 'name': 'National Institute of Biomedical Imaging and Bioengineering', 'admin_org_id': '12500035', 'admin_funding_url': ''}. PI: Reimund  Bayerlein. Award: $80500.
Start: 2026-08-07T00:00:00.
Project Summary/Abstract
Deep learning (DL)-based image denoisers have gained significant attention in positron emission tomography
(PET) due to their ability to enhance image quality in low-count or short-duration scans. These models hold
substantial promise for reducing radiation dose, minimizing scan time, and improving diagnostic performance –
particularly for vulnerable populations and in resource-limited settings. However, DL-based denoisers may
introduce artifactual features, including false-positive or false-negative lesions, that are not supported by the
underlying data. These artifacts remain poorly understood and pose a critical barrier to the safe clinical adoption
of AI-enhanced image reconstruction.
This project aims to systematically evaluate, quantify, and model the risk of artifacts in DL-denoised PET images.
Using a Monte Carlo-based lesion embedding framework developed by our team (DIANA), we will simulate
artificial hepatic lesions in real human PET data to establish in-vivo ground truth. Low-count images of various
noise levels will be generated through random downsampling of high-quality PET data acquired on the
EXPLORER total-body PET/CT scanner. These images will subsequently be denoised using a state-of-the-art
3D diffusion probabilistic model, and artifacts will be detected using a novel combination of gradient-domain
image differencing and generalized scan statistics.
Furthermore, we will systematically investigate how imaging and anatomical factors that are available at scan
time influence the probability of wrongfully added lesions in DL-denoised PET images. To that end we will develop
a logistic regression model to quantify the conditions under which artifacts are most likely to occur, thus, providing
a prototype predictive framework for risk stratification and clinical decision support. Input features will include
image noise level as well as anatomical and patient-specific variables (e.g., BMI, and amount of injected
radioactivity). This work enables a scientific assessment of the safety and fidelity of AI-based denoising methods,
ensuring they support rather than compromise clinical decision-making and the broader goals of translational
science.

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