Positron emission tomography (PET) with long axial field-of-view (LAFOV) scanners generates unprecedentedly large datasets, posing major challenges for conventional reconstruction algorithms. Recent advances in deep learning have improved PET image quality, yet scaling to high-dimensional data remains computationally demanding. Quantum computing (QC) offers new opportunities to address such challenges. This work presents a pilot study on image denoising using a hybrid quantum-classical autoencoder. The model employs a classical encoder, a quantum bottleneck implemented via a parameterized quantum circuit, and a classical decoder. Using simulated experiments on the medical modified national institute of standards and technology (MedMNIST) dataset with 28 × 28 grayscale images and on PET scan images with 64 × 64, results demonstrated that the quantum bottleneck can effectively recover structural details from noisy inputs. While scaling to large-volume PET data will require advances in quantum hardware, circuit design, and error mitigation, these results highlight the potential of hybrid quantum–classical approaches to complement deep learning in PET reconstruction.
Hu et al. (Fri,) studied this question.