MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
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/.
