Abstract #3019

Assessing Image Quality Metric Alignment with Radiological Evaluation in Datasets with and without Motion Artifacts

Elisa Marchetto^1,2,3, Hannah Eichhorn^4,5, Daniel Gallichan³, Stefan T. Schwarz^6,7,8, Nitesh Shekhrajka⁹, and Melanie Ganz^10,11

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ³CUBRIC, School of Engineering, Cardiff University, Cardiff, United Kingdom, ⁴Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany, ⁵School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ⁶University Hospitals of Wales, Department of Radiology, Cardiff, United Kingdom, ⁷CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁸University of Nottingham, School of Medicine, Nottingham, United Kingdom, ⁹University of Iowa hospitals and Clinics, Iowa City, IA, United States, ¹⁰Department of Computer science, University of Copenhagen, Copenhagen, Denmark, ¹¹Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark

Synopsis

Keywords: Data Processing, Data Processing

Motivation: A quantitative evaluation of image quality is crucial in various aspects of MRI, such as developing and validating new image reconstruction and artifact correction techniques. Currently, no image quality metric covers all possible artifacts, making it difficult to choose the right quality measure.

Goal(s): Evaluate consistency and reliability of image quality metrics in relation to image pre-processing and radiologists assessment.

Approach: We studied the correlation of ten commonly used quality metrics with radiological evaluations in datasets with and without motion.

Results: SSIM and PSNR had the strongest correlation with observer scores. Among reference-free metrics, Image Entropy and AES consistently showed strong correlations.

Impact: Automatically evaluating the quality of MR images is crucial. Our results show variability in the correlation between image-quality metrics and radiologists scores across datasets, highlighting the need for preprocessing optimization especially when no reference image is available.

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