2013 Summer Project Week: Individualized Neuroimaging Content Analysis using 3D Slicer in Alzheimer's Disease

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  Projects List

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  Individualized Neuroimaging Content Analysis in Alzheimer's Disease

Key Investigators

• USYD: Sidong Liu, Weidong (Tom) Cai
• BWH: Sonia Pujol, Ron Kikinis

Introduction

We have accumulated a wealth of knowledge learnt from the population-based research. Then how can we apply such knowledge to health care of individual patient? The target of current medical image computing is shifting from population-based analysis to subject-specific analysis.

Objective

The goal of this project is to present a novel way of applying our knowledge to subject-specific neuroimaging content analysis using 3D Slicer. As an example, we will demonstrate some analysis results of a group of Alzheimer's patients using 3D Slicer.

Approach, Plan

There are three key steps of individualized content analysis. First, we need to extract the features from the data and analyze the data to identify the disease related patterns. Second, the prior knowledge of such patterns should be encoded in certain way, so that it can be integrated into current research platform. Finally, we need to apply the knowledge into subject-specific content analysis.

Progress

The data were originally readable in Matlab, now they are loadable in 3D Slicer. In our pervious studies, we have proposed a range of medical knowledge encoding and decoding methods, including voxel-based degenerative t-maps (ICIP 2010), sparse-autoencoded brain atrophy templates (SNM 2013, EMBC 2013), and biomarker-based disease-sensitive kernels (MICCAI 2013).

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

1. ITK Module
2. Slicer Module
   1. Built-in
   2. Extension -- commandline ("YES")
   3. Extension -- loadable
3. Other (Please specify)

References

• Cai, W., Liu, S., Wen, L., Eberl, S., Fulham, M., Feng, D.: 3D Neurological Image Retrieval with Localized Pathology-Centric CMRGLC Patterns. In: The IEEE 17th International Conference on Image Processing (ICIP 2010), pp. 3201-3204, Hong Kong (2010)
• Liu, S., Cai, W., Song, Y., Pujol, S., Kikinis, R., Wen, L., Feng, D.: Sparse Auto-encoded Hypo-metabolism Patterns in Alzheimer's Disease and Mild Cognitive Impairment. The Journal of Nuclear Medicine (Abstract) (Accepted)
• Liu, S., Cai, W., Song, Y., Pujol, S., Kikinis, R., Feng, D.: Localized Sparse Code Gradient in Alzheimer's Disease Staging. In: The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka, Japan (2013)
• Liu, S., Song, Y., Cai, W., Pujol, S., Kikinis, R., Wang, X., Feng, D.: Multifold Bayesian Kernelization in Alzheimer’s Diagnosis. In: MICCAI 2013, Nagoya, Japan (Accepted)