Mapping Ventricular Expansion and its Clinical Correlates in Alzheimer's Disease and Mild Cognitive Impairment using Multi-Atlas Fluid Image Alignment & Overview Report of the 2009 SPIE Conference EunGyoung Han
2009 SPIE Medical Imaging Conference Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
2009 SPIE Medical Imaging Conference Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
2009 SPIE Medical Imaging Conference Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Sessions: Image Processing 1,11. Segmentation I-II 2. Statistical Models Segmentation Registration 3. Statistical Methods Atlas-based Methods Statistical 4, 5, 10. Registration I-III Methods & DTI Micellaneous 6. Motion Analysis 7. Vascular Image Processing 8. Atlas-based Methods 9. Keynote and Diffusion Tensor Imaging (Frontiers in D.I.--keynote) Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Sessions: Biomedical Applications in Molecular, Structural, and Functional Imaging 1. MR Brain Imaging Mr Brain and Mechanics 2. Keynote and Neuroimaging Neuroimaging Lung Motion Analys- 3. Lung is Blood flow and Miscellaneous 4. Blood Flow Tissue Micro- structure and 5. Tissue Microstructure Function and Function 6. Motion Analysis 7. Small Animal Imaging 8. Image-based Modeling 9, 10. Mechanics I-II 11. Clinical Applications Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Sessions: Visualization, Image-guided Procedures and Modeling 1. Neuro Neuro Robotics and Guidance Sys- tems 2, 9. Minimally Invasive I-II Liver Ultrasound Minimally Invasive Visulation and 3. Liver Geometry CT Guidance Registration 4. CT Guidance Cardiac Keynote and Modeling 5. Cardiac 6. Keynote and Modeling 7. Robotics and Guidance Systems 8. Ultrasound 10. Visualization and Geometry 11. Registration Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Alzheimer's Disease Mapping Ventricular Expansion and its Clinical Correlates in Alzheimer's Disease and Mild Cognitive Impairment using Multi-Atlas Fluid Image Alignment (Image Processing : Registration) Yi-Yu Chou1, Natasha Leporé1, Christina Avedissian1, Sarah K. Madsen1, Xue Hua1, Clifford R. Jack, Jr. 2, Michael W. Weiner3, Arthur W. Toga1, Paul M. Thompson1, and the Alzheimer's Disease Neuroimaging Initiative 1 Laboratory of Neuro Imaging, UCLA Department of Neurology, Los Angeles, CA, USA 2 Mayo Clinic College of Medicine, Rochester, MN 3 Depts. Radiology, Medicine & Psychiatry, UC San Francisco, San Francisco, CA Alzheimer's Disease Neuroimaging Initiative (ADNI) Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Alzheimer's Disease AD affecting 5~10% over age 65 30~40% over age 90 6~25% of MCI subjects per year transition to AD Testing subjects - 80 AD patients - 80 individuals with MCI - 80 healthy subjects Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Problem Statements and their Answers Do ventricular measures show a relatively high effect size in distinguishing disease from normality? • Ventricular expansion appears to provide the greatest sensitivity as a quantitative marker of disease progression in Alzheimer's Disease (AD) in serial MRI studies. How can we quantify the factors affecting progression from Mild Cognitive Impairment (MCI) to AD or normal aging to AD? • By developing automated brain mapping techniques to map and analyze lateral ventricular expansion. • By discovering which one of these automated techniques is optimal for such a task. Result is that we should now be able to detect which therapeutic factors may help patients resist neurodegeneration in drug trials. Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Automated Lateral Ventricle Segmentation and Shape Modeling - Lateral ventricular volumes automatically estimated for scans using a “multi-atlas” technique Method pipeline: 1) Map multiple surface-based atlases into each scan via fluid registration 2) Combine multiple segmentations of the same scan into a single average surface mesh 3) Randomly choose image samples and manually trace the lateral ventricles in contiguous coronal brain sections 4) Convert lateral ventricular surface into parametric meshes 5) Do fluid registration of each atlas and the embedded mesh models to all other subjects, treating the deforming images as Navier-Stokes viscous fluid, thereby guaranteeing a diffeomorphic mapping. 6) Apply fluid transforms to the manually traced ventricular boundary using tri-linear interpolation, generating a propagated contour on the unlabeled images 7) Match grid-points from corresponding surfaces across subjects to obtain group average parametric meshes Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Methods Flowchart Multiple surface meshes are Parametric Surface Map Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Methods Flowchart Medial curve - 3D curve traced out by the centroid of the ventricular boundary The medial curve defined in each individual before averaging the surfaces. Measure radial ventricular expansion in each individual Plot the resulting statistics on the average surface Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Ventricular Statistical Maps and Analysis Surface contractions and expansions were compared between groups at equivalent locations using Student's t-tests with 2-tails, and were correlate with different clinical characteristics including diagnosis, cognitive scores, ApoE genotype, clinical scores, and future decline. CDF plots of p-values determined the method's statistical power for finding links between morphology and different disease measures. p-value : describes the uncorrected significance of group differences, plotted onto the average surface model as a color-coded map q-value : gives single overall measure of significance for each p-map. If the q-value DNE, then there is insufficient evidence to reject null hypothesis. Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Ventricular Statistical Maps and Analysis Multi-statistical test CDF plot intersects with the y=20x line ==> the highest values for which at most 5% false positive are expected in the map. ==> observed p-values are limited to the [0 0.05] q-value (intersection point CDF and y=20x) ==> single overall measure of significance for each p-map. no intersection point => no evidence to reject the null hypothesis To assign overall significance values to each statistical map use false discovery rate (FDR) based on the expected proportions of voxels with intensity above the threshold under the null hypothesis. Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Linking Ventricular Morphology and Clinical Characteristics Significance maps map correlations between local ventricular enlargement and (1) diagnosis (MCI vs. normal, AD vs. normal and AD vs. MCI); (2) cognitive scores (MMSE, Global clinical dementia rate (CDR), and sum of Boxes CDR); (3) clinical depression scores, (4) ApoE genotype and (5) educational level Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Linking Ventricular Morphology and Clinical Characteristics CDFs of significance maps associating ventricular enlargement with diagnosis and clinical measures. Based on FDR q-values, the AD vs. control and MCI vs. control contrast are significant, as are the links between ventricular dilation (expansion) and (1) MMSE scores, and (2) depression. Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Predicting Future Cognitive Change How do changes detected by brain imaging predict future clinical decline? =>Their experiments correlated baseline ventricular morphology with subsequent change over 1 year in MMSE, global clinical dementia rate (CDR), sum of boxes CDR scores Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
Predicting Future Cognitive Change Significance maps correlate baseline ventricular shape with subsequent decline, over the following year in 3 commonly used clinical scores FDR analysis of future changes. Correlations were significant between baseline ventricular enlargement and future change in MMSE, Global CDR and Sum of Boxes scores. Scientific Computing and Imaging Institute, University of Utah Scientific Computing and Imaging Institute, University of Utah
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