Meeting Banner
Abstract #0336

Assessing the Influence of Preprocessing on the Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion

Hannah Eichhorn1,2,3, Elisa Marchetto3,4,5,6, Daniel Gallichan6, Julia A. Schnabel1,2,7, and Melanie Ganz8,9
1Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany, 2School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 3These authors contributed equally to this work., ., Germany, 4Bernard and Irene Schwartz Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, United States, 5Center for Advanced Imaging Innovation and Research (CAI2R), Dept. of Radiology, NYU School of Medicine, New York, NY, United States, 6CUBRIC, School of Engineering, Cardiff University, Cardiff, United Kingdom, 7School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, 8Department of Computer Science, University of Copenhagen, Copenhagen, Denmark, 9Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark

Synopsis

Keywords: Motion Correction, Artifacts, Motion Artifacts, Analysis/Processing, Brain, Image Quality Evaluation

Motivation: Reliable image quality assessment is essential for improving motion correction methods. However, common image quality metrics (IQMs) often show inconsistent agreement with radiological evaluations and lack standardization in preprocessing techniques.

Goal(s): Evaluating the correlation of ten common IQMs with radiological assessments and investigating how preprocessing steps affect these correlations.

Approach: We compare the IQMs on two brain imaging datasets with real motion artifacts and analyze the effects of preprocessing choices like normalization, region-of-interest masking and slice reduction.

Results: Reference-based IQMs exhibit stronger, more consistent correlations with radiological assessments than reference-free IQMs. Preprocessing steps, particularly normalization and brain masking, significantly influence the correlation.

Impact: This study underscores the critical role of preprocessing choices for reliable image quality evaluation. We strongly recommend documenting all preprocessing steps in future studies. Our results further demonstrate that reference-based metrics correlate more reliably with radiological assessments than reference-free metrics.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords