Abstract #0453

Motion corrected reconstruction of abdominal SWEEP data using local similarity graphs and deformable slice to volume registration

Laurence H Jackson¹, Alena Uus¹, Dafnis Batalle^2,3, Jana M Hutter¹, Thomas A Roberts¹, Anthony N Price¹, Alison Ho^2,4, Laura McCabe², Maria Deprez¹, Lucy Chappell⁴, Mary Rutherford², and Joseph V Hajnal^1,2

¹Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, Kings College London, London, United Kingdom, ²Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, Kings College London, London, United Kingdom, ³Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, United Kingdom, ⁴Department of Women and Children’s Health, School of Life Course Sciences, Kings College London, London, United Kingdom

In this work we introduce a novel pipeline for motion correction of SWEEP style acquisition data. The method utilizes local similarity graphs for efficient generation of static volumes by extracting the most coherent slices within a local neighborhood and interpolating over missing data. These static volumes are then used as registration targets for a patch-based deformable slice-to-volume registration. The pipeline produces highly coherent 3D volumes and is demonstrated in adult abdominal and fetal/placental imaging using 2D SWEEP bSSFP and SPGR acquisitions.

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