Low-dose computed tomography perceptual image quality assessment
Medical Image Analysis, vol. 99, pp. 103343
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.
Authors 25
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Affiliation as printed
Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Republic of Korea. Electronic address: ewonkyong@ewhain.net
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Affiliation as printed
Universitat Pompeu Fabra, Plaça de la Mercè, 12, Ciutat Vella, Barcelona 08002, Spain
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Rensselaer Polytechnic Institute
Affiliation as printed
Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA
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Rensselaer Polytechnic Institute
Affiliation as printed
Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA
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Rensselaer Polytechnic Institute
Affiliation as printed
Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA
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Affiliation as printed
Xi'an Jiaotong University, 28, Xianning West Road, Xi'an City, Shaanxi Province 710049, People's Republic of China
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Affiliation as printed
Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA
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Affiliation as printed
Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA
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Affiliation as printed
Chungnam National University College of Medicine, 266 Munghwa-ro, Daejeon 35015, Republic of Korea
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Harvard University · Massachusetts General Hospital
Affiliation as printed
MGH and Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
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Yonsei University · Chungnam National University
Affiliation as printed
Chungnam National University College of Medicine, 266 Munghwa-ro, Daejeon 35015, Republic of Korea
Yonsei University, A50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
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Affiliation as printed
Chungnam National University College of Medicine, 266 Munghwa-ro, Daejeon 35015, Republic of Korea
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Affiliation as printed
Chungnam National University College of Medicine, 266 Munghwa-ro, Daejeon 35015, Republic of Korea
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Delft University of Technology
Affiliation as printed
Delft University of Technology, Mekelweg 5, CD Delft 2628, Netherlands
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Donghang Lyu Aachen
Affiliation as printed
Leiden University, Rapenburg 70, EZ Leiden 2311, Netherlands
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Delft University of Technology
Affiliation as printed
Delft University of Technology, Mekelweg 5, CD Delft 2628, Netherlands
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Delft University of Technology
Affiliation as printed
Delft University of Technology, Mekelweg 5, CD Delft 2628, Netherlands
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Universidad San Francisco de Quito
Affiliation as printed
Universidad San Francisco de Quito, Campus Cumbayá, Diego de Robles s/n, Quito 170901, Ecuador
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Affiliation as printed
Mayo Clinic, 200 First St., SW Rochester, MN 55905, USA
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Affiliation as printed
Yonsei University, A50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
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Yonsei University · Chungnam National University
Affiliation as printed
Chungnam National University College of Medicine, 266 Munghwa-ro, Daejeon 35015, Republic of Korea
Yonsei University, A50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
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Friedrich-Alexander-Universität Erlangen-Nürnberg
Affiliation as printed
Friedrich-Alexander-Universität Erlangen-Nürnberg, Schloßplatz 4, Erlangen 91054, Germany
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Affiliation as printed
Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
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Affiliation as printed
Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
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Affiliation as printed
Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Republic of Korea; Computational Medicine, Graduate Program in System Health Science and Engineering, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea. Electronic address: choij@ewha.ac.kr
Cited by 31 stored of 32
Cited by patents worldwide 1 (Lens.org)
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Medical image quality evaluation method and system based on image processingCN121169848A 2025-12-19 Active