Abstract #4616

Integrating Inline Quality Control at the MRI Scanner: Global and Local Assessment of Motion Artifacts Using Deep Learning

Veronika Ecker^1,2, Melanie Ganz^3,4, Hannah Eichhorn^5,6, Elisa Marchetto^7,8, Till Huelnhagen⁹, Bin Yang², Sergios Gatidis¹, and Thomas Küstner¹

¹Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany, ²Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, ³Department of Computer Science, University of Copenhagen, Copenhagen, Denmark, ⁴Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark, ⁵Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany, ⁶School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ⁷Dept. of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York City, NY, United States, ⁸Dept. of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York City, NY, United States, ⁹MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany

Synopsis

Keywords: Motion Correction, Artifacts, Image Quality Assessment, Clinical Translation, Self-supervised Contrastive Learning

Motivation: MRI is essential for diagnostics, yet motion artifacts from patient movement can degrade image quality, risking misdiagnosis and necessitating rescans.

Goal(s): Our goal is to provide inline quality assessment of MRI scans to reduce rescans and improve diagnostic accuracy.

Approach: We implemented a deep-learning-based framework that evaluates image quality on a global and local level. The framework generates quality reports that can be displayed on the MR console.

Results: The framework reliably identified motion artifacts in abdominal and brain scans, providing practical quality feedback.

Impact: Our inline integration assessment for global and local image quality in MRI scans enables reliable detection of motion artifacts. This advancement allows for immediate corrective actions, improving diagnostic accuracy and optimizing imaging workflows.

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