Abstract #0516

HERON: High-Efficiency Real-Time mOtion quantification and re-acquisitioN for Fetal dMRI

Jordina Aviles Verdera^1,2, Antonia Bortolazzi³, Sara Neves Silva^1,2, Kamilah St Clair^1,2, Sarah McElroy^1,2,4, Sebastien Ourselin⁵, Raphael Tomi-Tricot^1,2,4, Mary A Rutherford^1,2, Joseph V Hajnal^1,2, and Jana Hutter^1,2,3

¹Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging, King's College London, London, United Kingdom, ²Early Life Imaging Research Department, School of Biomedical Engineering and Imaging, King's College London, London, United Kingdom, ³Smart Imaging Lab, Radiologisches Institut, Universitätsklinikum Erlangen, Erlangen, Germany, ⁴MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, ⁵School of Biomedical Engineering and Imaging, King's College London, London, United Kingdom

Synopsis

Keywords: Motion Correction, Motion Correction, Fetal, low-field

Motivation: Fetal diffusion MRI (dMRI) is essential for studying brain development but is highly sensitive to motion artifacts from fetal movement and maternal breathing, impacting data quality and limiting diagnostic accuracy.

Goal(s): To create a robust, real-time automatic pipeline (HERON) that enhances fetal brain dMRI by performing motion assessment and re-acquisition.

Approach: A pipeline using AI-driven brain localization, segmentation, and motion assessment on a clinical 0.55T scanner to autoplan, quality-check, and reacquire motion-affected dMRI volumes.

Results: Tested in 6 cases, the pipeline effectively improved image quality, reducing inter-volume motion and enabling more reliable ADC analysis even in challenging cases.

Impact: Enhancing fetal diffusion MRI through an automatic AI-driven pipeline enabling automatic planning, real-time quality checks and prospective re-acquisition of corrupted volumes enables higher diagnostic quality and wider use.

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