Multimodal Brain Image Analysis

This book constitutes the refereed proceedings of the Second International Workshop on Multimodal Brain Image Analysis, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 19 revised full papers presented were carefully reviewed and selected from numerous submissions. The objective of this workshop is to forward the state of the art in analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications.

Fast track conference proceedings Unique visibility State of the art research

Inhalt

Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning.- Combining DTI and MRI for the Automated Detection of Alzheimer's Disease Using a Large European Multicenter Dataset.- Genetics of Path Lengths in Brain Connectivity Networks: HARDI-Based Maps in 457 Adults.- Connectivity Network Breakdown Predicts Imminent Volumetric Atrophy in Early Mild Cognitive Impairment.-Deconfounding the Effects of Resting State Activity on Task Activation Detection in fMRI.- Hyperbolic Ricci Flow and Its Application in Studying Lateral Ventricle Morphometry.- Do We Really Need Robust and Alternative Inference Methods for Brain MRI.- Sparse Patch-Based Label Fusion for Multi-Atlas Segmentation.- How Many Templates Does It Take for a Good Segmentation?: Error Analysis in Multiatlas Segmentation as a Function of Database Size.- A Generative Model for Probabilistic Label Fusion of Multimodal Data.- Spatial Normalization of Diffusion Tensor Images with Voxel-Wise Reconstruction of the Diffusion Gradient Direction.- Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases.- Comparative Characterisation of Susceptibility Weighted MRI for Brain White Matter Lesions in MS.- Constructing Fiber Atlases for Functional ROIs via fMRI-Guided DTI Image Registration.- Structural Feature Selection for Connectivity Network-Based MCI Diagnosis.- Groupwise Segmentation Improves Neuroimaging Classification Accuracy.- DWI Denoising Using Spatial, Angular, and Radiometric Filtering.- MRI Estimation of T 1 Relaxation Time Using a Constrained Optimization Algorithm.- Robust Cerebral Blood Flow Map Estimation in Arterial Spin Labeling.