PIPPI 2026 Schedule
Central European Summer Time
27th September 2026, Strasbourg Convertion Center, France
13:30 - 13:40 Welcome & Introduction
13:40 - 14:30 PIPPI Keynote Prof Thomas Küstner , University Hospital of Tübingen, Germany
14:30 - 15:00 Introduction to Poster session and 2min Power Pitch Poster Presentations
P1: SUPER-IVIM-DC-BOOT: A Bootstrapped Physics-Informed IVIM Framework for Robust Placental Microstructure Analysis in Uncontrolled Maternal Diabetes, Naama Gavrielov (Technion - Israel Institute of Technology) et al.
P2: Quality over quantity: data curation for automated angle of progression estimation in intrapartum ultrasound, Mareen Kalis (Universität Bern) et al.
P3: The effect of image resolution on texture and shape features derived from placental magnetic resonance images, Suzie Coldman ( University of Sheffield) et al.
P4: Diffusion MRI Tract Analysis Separates Infants with Down Syndrome from Typical Controls, Martin Andreas Styner (University of North Carolina) et al.
P5: Mapping Structural Connectivity in the Neonatal Developing Brain using Multi-Component Tissue Modelling, Jaloliddin Rustamov (KU Leuven) et al.
P6: Multi-modal deep learning for fetal corpus callosum segmentation and characterization in MRI, Marina Di Stefano (University Medical Centre Utrecht) et al.
P7: Self-Tuning Age-Conditioned Choroid Plexus Segmentation in Neonatal MRI, Junghwa Kang (Hankuk University of Foreign Studies) et al.
P8: STORK: Spatio-Temporal Observation of uterine contRactions via neural networKs, Melissa Schween (Universität Hannover) et al.
P9: Region-Wise Interpretable Neonatal Brain Age Prediction via Pretrained Segmentation-Guided Attention, Dayeon Bak (Hankuk University of Foreign Studies) et al.
P10: Automated Fetal Brain MRI Biometry in Healthy and Pathological Cases, Ema Masterl (Univerza v Ljubljani) et al.
P11: cSVR: Fast Convolutional Slice-to-Volume Reconstruction for Fetal Brain MRI, Margherita Firenze (Massachusetts Institute of Technology) et al.
P12: Longitudinal registration of human fetal brain MRI, Florian Scalvini (IMT Atlantique) et al.
15:00 - 16:00 Break & Poster Session
16:00 - 17:00 Oral Session (8min presentation + 2min Q&A each)
16:00 - R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound, Yuanhan Wang (Hangzhou Dianzi University) et al.
16:10 - Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms, Yingcheng Liu (Massachusetts Institute of Technology) et al.
16:20 - EMD-Regularized Heatmap Regression for 3D Ultrasound Prenatal Facial Landmark Detection, Yusuf Baran Tanriverdi (Universitat Pompeu Fabra) et al.
16:30 - Shift functions with a spatial block bootstrap for within-subject comparison of placental T2* distributions under maternal hypoxia in a nonhuman primate model, Diego Fajardo-Rojas (King’s College London) et al.
16:40 - High-resolution multi-modal postmortem MRI of the human infant brain: methods for cortical reconstruction and anatomical parcellation, Pulkit Khandelwal (Massachusetts General Hospital, Harvard University), et al.
16:50 - Shape-DNA Spectral Analysis of Lesion Morphotypes in Paediatric-Onset Multiple Sclerosis, Prateek Mittal (Motilal Nehru National Institute Of Technology) et al.
17:00 - 17:50 PIPPI Circle Discussion Group - Where will our field be in 5 years time?
17:50 - 18:00 PIPPI Best Paper Award & Close
PIPPI 2026 Keynote Talk
Prof Thomas Küstner , University Hospital of Tübingen, Germany
From raw data to biomarkers: Learning-Based Methods for Motion-Robust MRI
Prof. Dr.-Ing. Thomas Küstner (Member, IEEE; Junior Fellow, ISMRM) is Chair of the Medical Imaging and Data Analysis (MIDAS.lab) at the University Hospital of Tübingen, Germany. He received his PhD from the University of Stuttgart in 2017 and subsequently worked as a Postdoctoral Researcher at the School of Biomedical Engineering and Imaging Sciences, King’s College London (2018–2021).
He chairs the Artificial Intelligence Expert Group of the German National Cohort (NAKO) and is a member of the governing committee of the ISMRM Motion Detection and Correction Study Group. He is also among the organizers of the autoPET challenge series.
His research focuses on AI-driven data processing for multimodal medical imaging (MRI, PET, CT), spanning acquisition, reconstruction, and analysis, as well as the development of interpretable AI methods for clinical and population-scale studies. His particular interest include MR-based motion imaging, motion correction, and advanced reconstruction methods.
