Positron emission tomography (PET) is commonly used in clinical settings to diagnose cancers, neurodegenerative diseases, and cardiac disorders. When the collected data are limited—either due to reduced injected dose, dynamic imaging, or faster acquisition—the reconstructed images become more uncertain, making interpretation more difficult. Classical model-based iterative reconstruction methods are robust but perform poorly when modeling the underlying medical images, resulting in degraded reconstructions under high noise conditions. Conversely, deep learning–based methods can improve reconstructed image quality, but often at the cost of instabilities linked to measurement noise and to the limited size of training datasets.
Une approche hybride
A
team from BioMaps (SHFJ) introduces a hybrid approach known as Plug-and-Play
(PnP), which leverages the strengths of both classical modeling and machine
learning, while scaling efficiently to high-dimensional problems such as PET.
An optimization algorithm simultaneously enforces consistency between the
reconstructed image and the measured data, as well as between the image and the
learned properties of medical images from a training set, all within a
mathematically controlled framework. The neural network is trained to guarantee
convergence of the entire process, using during training the fixed-point
equation satisfied at convergence.
Validated on realistic low-dose simulations and applied to patient data, this reconstruction method is competitive with “classical” model-based approaches and state-of-the-art learning-based reconstruction techniques. It better preserves fine structures, reduces noise in homogeneous regions, and generalizes remarkably well to unseen cases, including simulated lesions or pathological examples absent from the training set for real patient data.
This method offers a promising avenue for reducing PET injected dose without sacrificing diagnostic accuracy. It also opens perspectives for other imaging modalities where stability and image quality are essential.
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