标题： MS-IQA：一种用于PET/CT图像质量评估的多尺度特征融合网络 显示英文标题

标题： MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment

摘要： 正电子发射断层扫描/计算机断层扫描（PET/CT）在医学成像中起着关键作用，结合功能和解剖信息以帮助准确诊断。 然而，由于噪声、压缩和其他因素导致的图像质量退化可能会导致诊断不确定性并增加误诊的风险。 在评估PET/CT图像的质量时，低级特征如失真和高级特征如器官解剖结构都会影响图像的诊断价值。 然而，现有的医学图像质量评估（IQA）方法无法同时考虑这两种特征类型。 在这项工作中，我们提出了MS-IQA，一种用于PET/CT IQA的新颖多尺度特征融合网络，该网络利用ResNet和Swin Transformer各个中间层的多尺度特征，增强了其感知局部和全局信息的能力。 此外，还引入了一个多尺度特征融合模块，通过动态加权通道注意机制有效地结合高级和低级信息。 最后，为填补PET/CT IQA数据集的空白，我们构建了PET-CT-IQA-DS数据集，该数据集包含2,700张具有不同质量的PET/CT图像，并由放射科医生分配了质量评分。 在我们的数据集和公开可用的LDCTIQAC2023数据集上的实验表明，我们的模型在各种IQA指标上优于现有最先进的方法。 这项工作为PET/CT提供了一种准确且高效的IQA方法。 我们的代码和数据集可在https://github.com/MS-IQA/MS-IQA/获取。 显示英文摘要

摘要： Positron Emission Tomography / Computed Tomography (PET/CT) plays a critical role in medical imaging, combining functional and anatomical information to aid in accurate diagnosis. However, image quality degradation due to noise, compression and other factors could potentially lead to diagnostic uncertainty and increase the risk of misdiagnosis. When evaluating the quality of a PET/CT image, both low-level features like distortions and high-level features like organ anatomical structures affect the diagnostic value of the image. However, existing medical image quality assessment (IQA) methods are unable to account for both feature types simultaneously. In this work, we propose MS-IQA, a novel multi-scale feature fusion network for PET/CT IQA, which utilizes multi-scale features from various intermediate layers of ResNet and Swin Transformer, enhancing its ability of perceiving both local and global information. In addition, a multi-scale feature fusion module is also introduced to effectively combine high-level and low-level information through a dynamically weighted channel attention mechanism. Finally, to fill the blank of PET/CT IQA dataset, we construct PET-CT-IQA-DS, a dataset containing 2,700 varying-quality PET/CT images with quality scores assigned by radiologists. Experiments on our dataset and the publicly available LDCTIQAC2023 dataset demonstrate that our proposed model has achieved superior performance against existing state-of-the-art methods in various IQA metrics. This work provides an accurate and efficient IQA method for PET/CT. Our code and dataset are available at https://github.com/MS-IQA/MS-IQA/.