TFKT V2:用于计算机断层扫描感知图像质量评估的以任务为中心的自然图像知识转移。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Medical Imaging Pub Date : 2025-09-01 Epub Date: 2025-05-28 DOI:10.1117/1.JMI.12.5.051805
Kazi Ramisa Rifa, Md Atik Ahamed, Jie Zhang, Abdullah Imran
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引用次数: 0

摘要

目的:计算机断层扫描(CT)图像质量的准确评估是确保诊断可靠性,同时尽量减少辐射剂量的关键。放射科医生的评估既费时又费力。现有的自动化方法通常需要具有预定义图像质量评估(IQA)分数的大型CT数据集,这些数据通常与临床评估不太一致。我们的目标是开发一种无参考的、自动化的CT IQA方法,该方法密切反映放射科医生的评估,减少对大型注释数据集的依赖。方法:我们提出了以任务为中心的知识转移(TFKT),这是一种基于深度学习的IQA方法,利用任务相似的自然图像数据集的知识转移。TFKT结合了混合卷积神经网络变压器模型,通过学习自然图像失真和人工注释的平均意见得分,实现准确的质量预测。该模型在自然图像数据集上进行预训练,并在低剂量计算机断层扫描感知图像质量评估数据上进行微调,以确保任务特定的适应性。结果:广泛的评估表明,所提出的TFKT方法有效地预测了与放射科医生在域内数据集上的评估相一致的IQA分数,并很好地推广到域外的临床儿科CT检查。该模型在不需要高剂量参考图像的情况下实现了鲁棒性。我们的模型能够在一秒钟内评估约30个CT图像切片的质量。结论:提出的TFKT方法为CT IQA提供了一种可扩展、准确、无参考的解决方案。该模型弥合了传统和基于深度学习的IQA之间的差距,提供了适用于现实世界临床环境的临床相关和计算效率高的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TFKT V2: task-focused knowledge transfer from natural images for computed tomography perceptual image quality assessment.

Purpose: The accurate assessment of computed tomography (CT) image quality is crucial for ensuring diagnostic reliability while minimizing radiation dose. Radiologists' evaluations are time-consuming and labor-intensive. Existing automated approaches often require large CT datasets with predefined image quality assessment (IQA) scores, which often do not align well with clinical evaluations. We aim to develop a reference-free, automated method for CT IQA that closely reflects radiologists' evaluations, reducing the dependency on large annotated datasets.

Approach: We propose Task-Focused Knowledge Transfer (TFKT), a deep learning-based IQA method leveraging knowledge transfer from task-similar natural image datasets. TFKT incorporates a hybrid convolutional neural network-transformer model, enabling accurate quality predictions by learning from natural image distortions with human-annotated mean opinion scores. The model is pre-trained on natural image datasets and fine-tuned on low-dose computed tomography perceptual image quality assessment data to ensure task-specific adaptability.

Results: Extensive evaluations demonstrate that the proposed TFKT method effectively predicts IQA scores aligned with radiologists' assessments on in-domain datasets and generalizes well to out-of-domain clinical pediatric CT exams. The model achieves robust performance without requiring high-dose reference images. Our model is capable of assessing the quality of 30 CT image slices in a second.

Conclusions: The proposed TFKT approach provides a scalable, accurate, and reference-free solution for CT IQA. The model bridges the gap between traditional and deep learning-based IQA, offering clinically relevant and computationally efficient assessments applicable to real-world clinical settings.

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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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