发表机构
Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University, West Lafayette, IN, 47907, USA; Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 46202, USA（高级分子影像在放射治疗（AdMIRe）研究实验室，健康科学学院，普渡大学，西拉法叶，IN，47907，美国; 放射肿瘤学部，印第安纳大学医学院，印第安纳波利斯，IN，46202，美国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对头颈癌，提出从CT扫描合成PET图像的双路径框架，由回归U-Net和条件生成对抗网络组成，经热点引导融合输出，可提供代谢信息辅助成像和临床决策，重建PET体积有一定误差指标，能定位病变但高代谢区有SUV低估。
AI 中文摘要
18F-FDG PET/CT在头颈癌的分期、治疗规划和反应评估中起着核心作用，但其采集存在限制。本文提出一个概念验证深度学习框架，直接从常规CT扫描合成类PET图像。对44名患者数据进行回顾性分析，采用五折交叉验证。框架由回归U-Net和条件生成对抗网络组成，输出通过热点引导拉普拉斯金字塔融合。该框架在重建的三维PET体积上取得了一定的误差指标，定性评估显示能准确定位病变且背景纹理逼真，不过在高代谢肿瘤区域存在SUV低估问题。
英文摘要
18F-FDG PET/CT plays a central role in staging, treatment planning, and response assessment for head and neck cancer by providing functional information that complements anatomical CT imaging. However, PET acquisition requires radiotracer administration, specialized infrastructure, and additional cost, limiting its availability for repeated imaging. We present a proof of concept deep learning framework for synthesizing PET like images directly from routine CT scans with the goal of providing complementary metabolic information that may support imaging triage and clinical decision support rather than replace diagnostic PET. Forty-four patients from the publicly available QIN-HEADNECK dataset were retrospectively analyzed using five fold cross-validation. We propose a fully three dimensional dual path architecture consisting of (i) a regression U-Net optimized for voxel-wise quantitative SUV estimation and (ii) a conditional generative adversarial network optimized for realistic PET texture. Their outputs are integrated using hotspot guided Laplacian pyramid blending, allowing quantitative information from the regression pathway to be preserved within metabolically active regions while leveraging adversarial texture synthesis elsewhere. The proposed framework achieved a mean absolute error of 0.00395, PSNR of 39.19 dB, and SSIM of 0.9634 on reconstructed three dimensional PET volumes. Qualitative evaluation demonstrated accurate localization of many FDG-avid lesions while producing anatomically realistic background texture. Consistent with previous CT to PET synthesis studies, the principal limitation was systematic underestimation of SUV within highly metabolically active tumor regions.
Comments25 pages, 11 figures
