Ninon Burgos CNRS Researcher Brain and Spine Institute Aramis Lab - PowerPoint PPT Presentation

Ninon Burgos CNRS Researcher Brain and Spine Institute – Aramis Lab Paris, France

2 Research area Decision Image Medical support processing imaging systems Translational research

3 PhD – University College London Combination of structural and functional imaging modalities PET/CT PET/MRI CT PET MRI

4 PhD – University College London Image synthesis for the attenuation correction of PET/MR data Clinical motivation ▷ Imperfect attenuation correction on PET/MR scanners � γ (511 keV) 180° γ � � e - � � γ e + PET without PET with γ (511 keV) Attenuation attenuation correction attenuation correction Objective ▷ Synthesise CT from MR images to correct for attenuation PET/MR data MRI CT

5 PhD – University College London Image synthesis for the attenuation correction of PET/MR data Method [Burgos et al., MICCAI, 2013], [Burgos et al., IEEE TMI, 2014], [Burgos et al., EJNMMI, 2015] ▷ Multi-atlas registration, propagation and fusion MRI-CT database Mapped MRIs & CTs LSIM 1 W 1 Target MRI Target CT LSIM 2 W 2 ... ... ... ... LSIM n W n Inter-subject Affine alignment Resampling Intensity fusion mapping

6 PhD – University College London Image synthesis for the attenuation correction of PET/MR data Evaluation ▷ Validation on FDG and Florbetapir (Aß) PET images: less [Burgos et al., EJNMMI, 2015] than 2% difference ▷ Independent multi-centre evaluation study: joint best [Ladefoged et al., NeuroImage, 2017] performance ▷ Evaluation using low resolution MR images in collaboration [Sekine, Burgos et al., JNM, 2016] with University Hospital Zurich and GE Healthcare CT FDG PET Aß PET CT FDG PET Aß PET With real CT Proposed

7 PhD – University College London Image synthesis for the attenuation correction of PET/MR data Technology development ▷ NiftySeg Method part of a software package for https://github.com/KCL- BMEIS/NiftySeg image segmentation and synthesis ▷ NiftyWeb Method available online as a web http://niftyweb.cs.ucl.ac.uk/ service tool for testing, already used 1900+ times program.php?p=PCT Transfer to clinical research ▷ Method routinely used on several research [Weston et al., Alzheimer’s & Dementia: Diagnosis and Disease Monitoring, 2016] studies at the Dementia Research Centre (UCL) [Lane et al., BMC Neurology, 2017] Transfer of technology ▷ Licensing agreement recently signed between Oncovision and UCL

8 Current & future work Clinical motivation ▷ Neuroimaging plays a crucial role in the understanding, diagnosis, and treatment of neurological disorders ▷ Challenge: joint analysis of multiple imaging modalities at several time points Improve the analysis of multimodal neuroimaging data, and thus improve the understanding and diagnosis of neurodegenerative diseases

9 Current work Automatic identification of abnormality patterns Objective ▷ Locate and characterise patterns of abnormality from multimodal imaging data Method [Burgos et al., MICCAI, 2015], [Burgos et al., CMMI, 2017], [Burgos et al., Submitted] Subject’s MRI ▷ Subject-specific model of Subject-specific PET model healthy appearance of healthy appearance ▷ Subject-specific map of Subject-specific abnormality abnormality map ... Subject’s real PET ... MRI-PET control dataset

10 Current work Automatic identification of abnormality patterns Healthy Alzheimer’s Semantic Frontotemporal control disease dementia dementia L PET Abnormality map PET Abnormality map PET Abnormality map PET Abnormality map SUVR Z-score 0 1 2 -3 0 3

11 Current work Automatic identification of abnormality patterns Visualisation tool Improved interpretability of subsequent analyses Alzheimer’s Semantic Frontotemporal disease dementia dementia Alzheimer’s disease