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Positron Emission Tomography (PET) is a critical imaging modality in nuclear medicine but requires radioactive tracer administration, which increases radiation exposure risks. While recent studies have investigated MR-guided low-dose PET denoising, they neglect two critical factors: the synergistic roles of multicontrast MR images and disease-specific denoising requirements. In this work, we propose a diffusion model that integrates T1-weighted, T2 fluid attenuated inversion recovery (T2 FLAIR), and hippocampal-optimized (T2 HIPPO) MR sequences to achieve ultra-low-dose PET denoising tailored for temporal lobe epilepsy (TLE). Our parallel cross-modal fusion (PCMF) module employs dedicated encoders to extract cross-modal features-which are dynamically integrated via attention mechanisms. Extensive experiments demonstrate that our method outperforms other approaches in preserving image quality. The PSNR and SSIM obtained were 37.0251 $\pm$ 1.5215 dB and 0.9760 $\pm$ 0.0057 (p<0.01). Compared to the PET-only baseline model (IDDPM), our method achieved improvements of 8.4% in PSNR and 1.7% in SSIM, particularly excelling in diagnostically relevant temporal and hippocampal regions. This method provides a novel pathway for disease-specific PET denoising and has the potential to be generalized to other neurodegenerative diseases.
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http://dx.doi.org/10.1109/JBHI.2025.3606960 | DOI Listing |
IEEE J Biomed Health Inform
September 2025
Positron Emission Tomography (PET) is a critical imaging modality in nuclear medicine but requires radioactive tracer administration, which increases radiation exposure risks. While recent studies have investigated MR-guided low-dose PET denoising, they neglect two critical factors: the synergistic roles of multicontrast MR images and disease-specific denoising requirements. In this work, we propose a diffusion model that integrates T1-weighted, T2 fluid attenuated inversion recovery (T2 FLAIR), and hippocampal-optimized (T2 HIPPO) MR sequences to achieve ultra-low-dose PET denoising tailored for temporal lobe epilepsy (TLE).
View Article and Find Full Text PDFJ Imaging Inform Med
August 2025
Champalimaud Clinical Centre, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal.
Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan's acquisition duration. This study assesses the clinical adequacy of restoring fast-acquisition F-fluorodeoxyglucose ([F]FDG) PET to its standard-of-care image quality through deep-learning-based (DL) methods. Fast and standard whole-body [F]FDG PET acquisitions of 117 oncological patients were included in the training and testing of three convolutional neural networks.
View Article and Find Full Text PDFComput Med Imaging Graph
September 2025
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
Introduction: Unsupervised deep learning methods can improve the image quality of positron emission tomography (PET) images without the need for large-scale datasets. However, these approaches typically require training a distinct network for each patient, making the reconstruction process extremely time-consuming and limiting their clinical applicability. In this paper, our research objective is to develop an efficient unsupervised learning framework for unsupervised PET image reconstruction, in order to fulfill the clinical requirement for real-time imaging capabilities.
View Article and Find Full Text PDFProc IEEE Int Symp Biomed Imaging
April 2025
Dept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
The to-be-denoised positron emission tomography (PET) volumes are inherent with diverse count levels, which imposes challenges for a unified model to tackle varied cases. In this work, we resort to the recently flourished prompt learning to achieve generalizable PET denoising with different count levels. Specifically, we propose dual prompts to guide the PET denoising in a divide-and-conquer manner, i.
View Article and Find Full Text PDFJ Imaging
July 2025
Department of Nuclear Medicine, Centre National PET, Centre Hospitalier de Luxembourg, 4 Rue Ernest Barblé, L-1210 Luxembourg, Luxembourg.
This study assesses the clinical deployment of SubtlePET™, a commercial AI-based denoising algorithm, across three radiotracers-F-FDG, Ga-PSMA-11, and F-FDOPA-with the goal of improving image quality while reducing injected activity, technologist radiation exposure, and scan time. A retrospective analysis on a digital PET/CT system showed that SubtlePET™ enabled dose reductions exceeding 33% and time savings of over 25%. AI-enhanced images were rated interpretable in 100% of cases versus 65% for standard low-dose reconstructions.
View Article and Find Full Text PDF