ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment.
Authors
Affiliations (9)
Affiliations (9)
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China; School of Information Engineering, Nanchang University, Nanchang 330031, China. Electronic address: [email protected].
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China; School of Information Engineering, Nanchang University, Nanchang 330031, China. Electronic address: [email protected].
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China; School of Information Engineering, Nanchang University, Nanchang 330031, China. Electronic address: [email protected].
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China. Electronic address: [email protected].
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China. Electronic address: [email protected].
- Department of Stomatology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China. Electronic address: [email protected].
- Department of Biological Sciences, School of Science, Xi'an Jiaotong Liverpool University, Suzhou 215123, China. Electronic address: [email protected].
- School of Computer Science and Engineering, Southeast University, Nanjing 210096, China. Electronic address: [email protected].
- School of Information Engineering, Nanchang University, Nanchang 330031, China. Electronic address: [email protected].
Abstract
In abdominal CT imaging, optimizing the balance between radiation dose and image quality is crucial, and the primary prerequisite is accurate image quality assessment. Clinical practice uses doctors' subjective judgment as the gold standard, but it is time-consuming and costly; therefore, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for evaluating CT image quality has significant practical value. This paper proposes a novel deep learning-based framework, ClinReadNet, whose design aligns with the clinical reading logic of radiologists: first, it introduces the Sobel ordinal quality network (SOQN) module, which can simultaneously focus on edge details highly relevant to image quality and the quality distribution pattern of the entire image, accurately matching the clinical image-reading judgment habit of "considering both local details and overall context"; second, the framework integrates the (shifted) window multi-scale temperature multi-head self-attention ((S)W-MTMSA) module, which further replicates the radiologists' image-reading process of shifting from overall scanning to local focusing, and accurately locks in regions of interest through multi-sharpness attention; third, it designs the hierarchical ranked probability score (HRPS) loss function, which combines the dual logics of coarse classification and fine classification, while paying attention to the distance information between grading labels, effectively improving the performance of image quality assessment. Experiments conducted on the LDCTIQAG2023 dataset show that the proposed method achieves the current state-of-the-art (SOTA) performance: the values of Pearson's linear correlation coefficient (PLCC), Spearman's rank-order correlation coefficient (SROCC), and Kendall's rank-order correlation coefficient (KROCC) reach 0.9507, 0.9554, and 0.8629 respectively, with the sum of their absolute values (Score) being 2.7690, outperforming existing methods.