arXiv 2510.248052025-10-28q-bio.QMcs.AIcs.CV

在高分辨率未校正PET图像上使用多视图集成条件扩散模型实现无CT衰减校正

CT-Less Attenuation Correction Using Multiview Ensemble Conditional Diffusion Model on High-Resolution Uncorrected PET Images

Department of Medical Imaging and Radiation Sciences（医学影像与放射科学系）；Université de Sherbrooke（Sherbrooke大学）；Sherbrooke Molecular Imaging Center（Sherbrooke分子影像中心）；CRCHUS；Nantes Université（Nantes大学）；Laboratoire CRCI2NA, INSERM, CNRS（CRCI2NA实验室，INSERM，CNRS）；Imaging Research and Technology (IR&T) Inc.（影像研究与技术公司）；Research Center on Aging, Department of Medicine（衰老研究中心，医学系）；Department of Electrical Engineering and Computer Engineering（电气工程与计算机工程系）

Alexandre St-Georges, Gabriel Richard, Maxime Toussaint, Christian Thibaudeau, Etienne Auger, Étienne Croteau, Stephen Cunnane, Roger Lecomte, Jean-Baptiste Michaud

备注：: This is a preprint and not the final version of this paper

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AI总结：

针对PET成像依赖CT进行衰减校正存在辐射、配准误差及成本高的问题，提出结合多视图与集成投票的条件DDPM方法生成伪CT，在159例头部扫描数据上实现了更优的校正效果。

摘要：

正电子发射断层扫描（PET）中的准确量化对于获得精准诊断结果和有效治疗随访至关重要。PET成像中遇到的一个主要问题是衰减。衰减指的是光子在到达探测器之前穿过生物组织时被检测到的数量减少的现象。当缺少这类校正或校正不足时，这种信号衰减会导致量化不准确，使得难以区分良性与恶性病变，还可能导致误诊。通常，这种校正是通过同步计算机断层扫描（CT）成像来完成的，以获取结构数据用于计算光子在全身的衰减情况。然而，这种方法会使患者受到额外的电离辐射照射，存在PET/CT成像序列之间潜在的空间配准不准的问题，并且需要昂贵的设备基础设施。神经网络架构的最新进展为合成CT图像生成提供了另一种替代方法。我们的研究表明，条件去噪扩散概率模型（DDPMs，Conditional Denoising Diffusion Probabilistic Models）能够从未经衰减校正的PET图像生成高质量的CT图像，以进行衰减校正。通过利用未经衰减校正的PET图像的所有三个正交视图，结合集成投票的DDPM方法能够生成质量更高的伪CT图像，减少伪影并提升层间一致性。一项使用Siemens Biograph Vision PET/CT扫描仪采集的159例头部扫描的研究结果表明，该方法在伪CT生成方面实现了定性和定量的双重提升。在CT图像上，该方法的平均绝对误差为32±10.4 HU；将使用生成的伪CT衰减图重建的PET图像与使用真实CT重建的PET图像相比，所有感兴趣区域的平均误差为(1.48±0.68)%。

英文摘要：

Accurate quantification in positron emission tomography (PET) is essential for accurate diagnostic results and effective treatment tracking. A major issue encountered in PET imaging is attenuation. Attenuation refers to the diminution of photon detected as they traverse biological tissues before reaching detectors. When such corrections are absent or inadequate, this signal degradation can introduce inaccurate quantification, making it difficult to differentiate benign from malignant conditions, and can potentially lead to misdiagnosis. Typically, this correction is done with co-computed Computed Tomography (CT) imaging to obtain structural data for calculating photon attenuation across the body. However, this methodology subjects patients to extra ionizing radiation exposure, suffers from potential spatial misregistration between PET/CT imaging sequences, and demands costly equipment infrastructure. Emerging advances in neural network architectures present an alternative approach via synthetic CT image synthesis. Our investigation reveals that Conditional Denoising Diffusion Probabilistic Models (DDPMs) can generate high quality CT images from non attenuation corrected PET images in order to correct attenuation. By utilizing all three orthogonal views from non-attenuation-corrected PET images, the DDPM approach combined with ensemble voting generates higher quality pseudo-CT images with reduced artifacts and improved slice-to-slice consistency. Results from a study of 159 head scans acquired with the Siemens Biograph Vision PET/CT scanner demonstrate both qualitative and quantitative improvements in pseudo-CT generation. The method achieved a mean absolute error of 32 $\pm$ 10.4 HU on the CT images and an average error of (1.48 $\pm$ 0.68)\% across all regions of interest when comparing PET images reconstructed using the attenuation map of the generated pseudo-CT versus the true CT.

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