Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models instigated the development of many methods to assess their performance. However, most IQA solutions are designed to predict image quality in the general domain, with the applicability to specific areas, such as medical imaging, remaining questionable. Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models. In this work, we aspire to fill these gaps by carrying out the most extensive IQA evaluation study for Magnetic Resonance Imaging (MRI) to date (14,700 subjective scores). We use outputs of neural network models trained to solve problems relevant to MRI, including image reconstruction in the scan acceleration, motion correction, and denoising. Our emphasis is on reflecting the radiologist's perception of the reconstructed images, gauging the most diagnostically influential criteria for the quality of MRI scans: signal-to-noise ratio, contrast-to-noise ratio, and the presence of artifacts. Seven trained radiologists assess these distorted images, with their verdicts then correlated with 35 different image quality metrics (full-reference, no-reference, and distribution-based metrics considered). The top performers -- DISTS, HaarPSI, VSI, and FID-VGG16 -- are found to be efficient across three proposed quality criteria, for all considered anatomies and the target tasks.
PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment
Chongqing Research Institute of Harbin Institute of Technology（哈尔滨工业大学重庆研究所）；Harbin Institute of Technology（哈尔滨工业大学）；Faculty of Computing（计算机学院）；School of Computer Science（计算机科学学院）；University of Macau（澳门大学）；Institute for Infocomm Research（信息与通信研究所以）
Engineering AI Tools for Systematic and Scalable Quality Assessment in Magnetic Resonance Imaging
University of Southampton（南安普顿大学）；University Hospital Southampton NHS Foundation Trust（南安普顿大学医院NHS基金会信托）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
AutoIQ: An Ensemble Framework for Automatic Assessment of Geometric Distortion in Prostate Diffusion-Weighted Imaging
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（生物医学成像研究 institute， Cedars-Sinai 医疗中心）；Department of Bioengineering, University of California（生物工程系，加州大学）；Siemens Medical Solutions USA Inc.（西门子医疗解决方案美国公司）；Siemens Healthineers AG（西门子健康影像股份有限公司）；Department of Imaging, Cedars-Sinai Medical Center（成像部，Cedars-Sinai 医疗中心）；Department of Nuclear Medicine, Cedars-Sinai Medical Center（核医学部，Cedars-Sinai 医疗中心）；Department of Urology, Cedars-Sinai Medical Center（泌尿科，Cedars-Sinai 医疗中心）
Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification
Institute of Information Science and Technologies, National Research Council（信息科学与技术研究所，国家研究理事会）；Institute of Agricultural Biology and Biotechnology, National Research Council（农业生物学与生物技术研究所，国家研究理事会）；Department of Agricultural, Food, and Agri-Environmental Sciences（农业、食品和农业环境科学系）
Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks
Medical University of Warsaw（华沙医科大学）；University of Warsaw（华沙大学）；SGH Warsaw School of Economics（SGH华沙经济学院）；Carolina Medical Center（卡罗莱纳医疗中心）；Warsaw University of Technology（华沙理工大学）；Tooploox
Assessment of Clonal Hematopoiesis of Indeterminate Potential and Future Cardiomyopathy from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population
Yale School of Medicine（耶鲁大学医学院）；University of Pennsylvania（宾夕法尼亚大学）
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；University Hospital of Tuebingen（图宾根大学医院）；University of Minnesota（明尼苏达大学）；New York University（纽约大学）；Friedrich-Alexander University Erlangen（埃尔朗根-纽伦堡大学）
