发表机构
School of Health Sciences, Purdue University; Indiana University School of Medicine（普渡大学健康科学学院; 印第安纳大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出LAFNO模型，用CT衍生的两个代理通道替代放射组学条件化，在TCIA数据集上实现CT到PSMA-PET合成，提升病灶活性估计与放射组学可重复性。
AI 中文摘要
从CT或MRI合成PET的深度学习模型可降低患者剂量并减少扫描仪需求，但通常采用L1或均方误差（MSE）等全局损失优化，对所有体素一视同仁。在全身PSMA-PET中，肿瘤体素仅占体积的一小部分，却承载着临床相关的活性信号；因此，模型可能在结构相似性指数测度（SSIM）和峰值信噪比（PSNR）上取得较高值，却仍低估病灶活性或无法保留肿瘤特异性结构。放射组学提供肿瘤强度和纹理的生物学意义描述符，但直接放射组学条件化耗时，因为需要从勾画的病灶区域提取特征。我们提出LAFNO，即病灶感知自适应傅里叶神经算子（Lesion-Aware Adaptive Fourier Neural Operator），用于CT到PSMA-PET的合成，它用两个高效的CT衍生代理通道替代高维放射组学条件化。受PSMA亲和肿瘤核心及瘤周区域放射组学分析的启发，LAFNO使用一个对比代理表征局部密度变化，一个无序代理表征局部纹理异质性，两者均注入模型瓶颈层。LAFNO结合了全体积重建与病灶层面的总病灶活性（TLA）、肿瘤核心对比及瘤周监督。我们在TCIA PSMA-PET-CT-Lesions数据集上，将LAFNO与四种基线架构进行评估。LAFNO在全体积图像质量上保持竞争力，对18F和68Ga-PSMA分别取得0.960和0.938的SSIM，同时将每患者TLA误差分别降至18F-PSMA的48.3%和68Ga-PSMA的64.0%，并在两种示踪剂的所有特征类别中实现最高的肿瘤核心放射组学可重复性。瘤周可重复性仍依赖于示踪剂，表明合成PSMA-PET的生物学保真度仍具挑战性。
英文摘要
Deep learning models that synthesize PET from CT or MRI can reduce patient dose and scanner demand, but are typically optimized with global losses such as L1 or mean squared error (MSE) that treat all voxels similarly. In whole-body PSMA-PET, tumor voxels occupy only a small fraction of the volume, yet carry the clinically relevant activity signal; as a result, models can achieve high structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) while still underestimating lesion activity or failing to preserve tumor-specific structure. Radiomics provides biologically meaningful descriptors of tumor intensity and texture, but direct radiomics conditioning is time-consuming because it requires feature extraction from delineated lesion regions. We propose LAFNO, a Lesion-Aware Adaptive Fourier Neural Operator for CT-to-PSMA-PET synthesis that replaces high-dimensional radiomics conditioning with two efficient CT-derived proxy channels. Motivated by radiomics analysis of PSMA-avid tumor core and peritumoral regions, LAFNO uses a contrast proxy for local density variation and a disorder proxy for local texture heterogeneity, both injected into the model bottleneck. LAFNO combines whole-volume reconstruction with lesion-level total lesion activity (TLA), tumor-core contrast, and peritumoral supervision. We evaluated LAFNO against four baseline architectures on the TCIA PSMA-PET-CT-Lesions dataset. LAFNO remained competitive on whole-volume image quality, achieving SSIM of 0.960 and 0.938 for 18F- and 68Ga-PSMA, respectively, while reducing per-patient TLA error to 48.3% and 64.0% for 18F- and 68Ga-PSMA, respectively, and achieving the highest tumor-core radiomics reproducibility across all feature classes for both tracers. Peritumoral reproducibility remained tracer-dependent, indicating that biological fidelity in synthetic PSMA-PET remains challenging.
