Low-dose computed tomography perceptual image quality assessment

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

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Publication Details

Journal
Medical Image Analysis
Published
2024-09-06
DOI
https://doi.org/10.1016/j.media.2024.103343
Citations
24
Primary Topic
Advanced X-ray and CT Imaging
Type
article
Field-Weighted Citation Impact
4.43

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article

Low-dose computed tomography perceptual image quality assessment

Xuanqin Mou, Md Atik Ahamed, Adrián Galdrán, Scott S. Hsieh et al.
24 citations
Medical Image Analysis
Advanced X-ray and CT Imaging
4.43
article

Low-dose computed tomography perceptual image quality assessment

Xuanqin Mou, Md Atik Ahamed, Adrián Galdrán, Scott S. Hsieh, Kyungsang Kim, Yongyi Shi, Fabian Wagner, Garry E. Gold, Wenjun Xia, Adam Wang, Boohwi Hong, Maria Baldeon Calisto, Wonkyeong Lee, Jang‐Hwan Choi, Minah Han, Ge Wang, Andreas Maier, Ji Eun Oh, Abdullah-Al-Zubaer Imran, Jongduk Baek, Philip Tempelman, Lars van Blokland, Jong Tak Baek, Adrian Kuiper, Donghang Lyu, Dongheon Lee
article en
24 citations

Abstract

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

Medical Image AnalysisVol. 99
Leiden University (NL), Mayo Clinic (US), Harvard University (US), Ewha Womans University (KR), University of Kentucky (US), Rensselaer Polytechnic Institute (US), Universitat Pompeu Fabra (ES), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Yonsei University (KR), Chungnam National University (KR), University of Applied Sciences Leiden (NL), Lemuel Shattuck Hospital (US), Universidad San Francisco de Quito (EC), Xi'an Jiaotong University (CN), Stanford University (US), Delft University of Technology (NL)
Korea Medical Device Development Fund, National Research Foundation of Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 5%
Advanced X-ray and CT Imaging
4.43
DOIOpen Access PDFOpenAlex
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