Authors: Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Georges EI Fakhri, Chi Liu

Published on: April 27, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2404.17994

Summary

• What is new: A lesion-perceived and quantification-consistent modulation strategy for PET image denoising that enhances lesion visibility and quantification accuracy without extra computational cost during inference.
• Why this is important: Existing PET image denoising methods blur important details, leading to inaccurate lesion quantification.
• What the research proposes: The proposed LpQcM strategy improves the denoising process by focusing on lesion visibility and accurate quantification across different noise levels and datasets.
• Results: Integrating LpQcM reduced the lesion SUVmean bias by 2.92% and increased the PSNR by 0.34 on average for extremely low-count images.

Technical Details

Technological frameworks used: A plug-and-play LpQcM strategy adaptable to various model architectures.

Models used: Auxiliary segmentation networks for lesion detection, multiscale quantification-consistent modulation.

Data used: Large PET datasets from multiple centers and vendors with varying noise levels.

Potential Impact

Healthcare imaging departments, PET imaging technology companies, and medical diagnostics firms.

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