Simulating Neurodegeneration through Longitudinal Population - PowerPoint PPT Presentation

Outline Background Simulating neurodegneration through longitudinal population analysis Simulating Neurodegeneration through Longitudinal Population Analysis of Structural and Diffusion Weighted MRI Data Modat et al. MICCAI 2014 Bishesh Khanal, Computer Vision Seminar Asclepios, INRIA Sophia Antipolis October 8, 2014 1 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 2 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 3 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Neurons, Gray Matter(GM), White Matter(WM) and Cerbral Spinal Fluid (CSF) Figure: Figure: Cortex.[ http://en.wikipedia.org/wiki/File: GM/WM.[ http://www.medinewsdigest.com/?p=3249 . Neuron_Hand-tuned.svg . Accessed on 08-01-2014] Accessed on 08-01-2014] 4 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Neurodegeneration and Longitudinal images Figure: Baseline and follow-up images of an Alzheimer’s Disease patient. Neurodegenration: Progressive loss of structure or function of neurons, including death of neurons [ wikipedia ] Longitudinal images: Time series images of a same subject. 5 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 6 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Dementia and Alzheimer’s Disease Dementia: Progressive decline of cognitive functions. Loss of memory, mood changes, and problem with communication and reasoning. AD: Most common cause of Dementia, mostly affects older people. Characterized by atrophy , Amyloid β ( A β ) plaques and Neurofibrillary tangles ( NFTs ). Figure: A β plaques and NFTs in AD [2000 BrightFocus Foundation] 7 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Multimodal neuroimaging for AD D ifferent modalities required to get different types of information. Figure: Tractography example Figure: Amyloid imaging and structural MRI [ http://en.wikipedia.org/wiki/File: DTI-sagittal-fibers.jpg ] 8 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 9 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Atrophy Measurement Figure: Baseline and follow-up images of an Alzheimer’s Disease patient. Segmentation based methods. Registration based methods. How to validate these methods ? 10 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Registration Figure: Nonlinear registeration of longitudinal images 11 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Registration Figure: Example displacement fields of longitudinal evolution 12 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Registration Figure: Registration overview [ Sotiras et al BIASS2013 ] 13 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 14 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Figure: Framework of the overall pipeline 15 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 16 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Weighting similarity measures from different modalities Similarity measure M that drives the registration: M ( B , F ; µ ) = α × M s ( B s , F s ( u ; µ )) + α × M s ( B s ( u − 1 ; µ ) , F s ) + β × M d ( B d , F d ( u ; µ )) + β × M d ( B d ( u − 1 ; µ ) , F d ) (1) where, s : Structural T1 image d : Diffusion weighted image B: Baseline image F: Followup image M s : locally normalised cross correlation summed over all voxels. M d : distance between the tensors, summed over all voxels. α , β : Weights (empirically set to 0.5) u : Deformation field µ : parameters, the cubic b-spline parameters. 17 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Template database Figure: Template database 18 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Background 1 Brain and Neurodegeration Alzheimer’s Disease and Multimodal NeuroImaging Atrophy Measurement and Registration Simulating neurodegneration through longitudinal population 2 analysis Overall Framework Multimodal Registration and Template database Simulating Flows Discussion 19 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Registering new subject For a new subject i with its T1w and DTI image B i , Register all baseline images from the template database to B i . Figure: Schematic for registrations between template and the subject 20 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Weighting the flows to create a new one Weight flows based on the distance of the template’s basline image to subject image distance. ( Bi , Bj ; u i j ) � � u i × e − D � j ◦ v j t j ∈ grp v grp = (2) i ( Bi , Bj ; u i j ) j ∈ grp e − D � t Simulated follow-up images F NC , F FTD and F AD simulated from i i i B i as: F grp = exp ( v grp ) ◦ B i i i 21 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Simulation Example Figure: Subject and disease-specific longtudinal changes simulator result 22 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Simulation Results Figure: Distance sorted in ascending order 23 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Simulation Results Figure: Distance sorted in ascending order 24 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Discussion Using multi-modal data decreases variance of registration results. (Needs further experimental verification). Flow propagation method: Parallel transport and other techniques ? Can we extend to ”learning of deformation fields” ? 25 / 26

Outline Background Simulating neurodegneration through longitudinal population analysis Thankyou! 26 / 26