Difference between revisions of "Algorithm:Utah2"

From NAMIC Wiki
Jump to: navigation, search
Line 26: Line 26:
 
== [[Projects:PathologyAnalysis|Analysis of Brain Images with Pathological Changes]] ==
 
== [[Projects:PathologyAnalysis|Analysis of Brain Images with Pathological Changes]] ==
  
<font color="red">'''Ongoing (Updated 04/2013): '''</font> Quantification, analysis and display of brain pathology as observed in MRI is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes and for studying treatment strategies.
+
<font color="red">'''Ongoing: '''</font> Quantification, analysis and display of brain pathology as observed in MRI is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes and for studying treatment strategies.
 
We conduct research on developing novel methodologies that covers registration, segmentation, visualization of pathological structures
 
We conduct research on developing novel methodologies that covers registration, segmentation, visualization of pathological structures
 
with the aim of quantifying changes due to lesions, bleeding, and deformations over time.
 
with the aim of quantifying changes due to lesions, bleeding, and deformations over time.

Revision as of 21:12, 11 October 2014

Home < Algorithm:Utah2
Back to NA-MIC Algorithms

Overview of Utah 2 Algorithms (PI: Guido Gerig)

The Utah II group, guided by Guido Gerig and Marcel Prastawa, is interested in providing new methodology for analysis of DTI (including group analysis, fiber tract parametrization and quantification, longitudinal analysis), of image data including pathology (lesions (MS, lupus), tumor, trauma etc.), and methodology for registration and segmentation of serial/longitudinal image data. Methodology development will focus on subject-specific analysis in personalized medicine applications, and on providing normative data in the form of spatial altlases and descriptive information on geometric and appearance change in the presence of pathology.

Utah 2 Projects

Ace modes final.png

Analysis of Longitudinal Shape Variability

Ongoing (Updated 08/2012): Statistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. In this project, we develop a new method for the statistical analysis of longitudinal shape variability. More...

Fishbaugh, J., Prastawa, M., Durrleman, S., Piven, J., Gerig, G. Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling. In N. Ayache et al. (Eds.): MICCAI 2012, Part I, LNCS 7510, pp. 730--737. (2012)

Segmentation TBI u.png

Analysis of Brain Images with Pathological Changes

Ongoing: Quantification, analysis and display of brain pathology as observed in MRI is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes and for studying treatment strategies. We conduct research on developing novel methodologies that covers registration, segmentation, visualization of pathological structures with the aim of quantifying changes due to lesions, bleeding, and deformations over time. More...

Bo Wang, Wei Liu, Marcel Prastawa, Andrei Irimia, Paul M. Vespa, John D. Van Horn, P. Thomas Fletcher, and Guido Gerig . 4D Active Cut: An Interactive Tool for Pathological Anatomy Modeling, In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, pp. 529-532. IEEE, 2014.

Namic tract longitudinal main.png

Tract-based longitudinal modeling of DTI data

Ongoing (Updated 08/2012): This project develops a methodology to explore subject-specific, DTI data obtained from brain's white matter tracts, available at multiple but often sparsely present timepoints. The aim is to have a continuous representation of the longitudinal diffusion changes along tract definitions. More...

Sharma, A., Durrleman, S. , Gilmore, J.H. , Gerig, G. Longitudinal growth modeling of discrete-time functions with application to DTI tract evolution in early neurodevelopment. Proc. of 9th IEEE International Symposium on Biomedical Imaging (ISBI May'12), p.1397-1400.

ColorFA-4D.png

Longitudinal analysis of DWI

Ongoing: Updated (08/2012) This project develops methodology to analyze serial/longitudinal DWI data, e.g. as baseline and follow-up in trauma, serial follow-up scans as acquired in the Huntington PREDICT study, or subject-specific white matter maturation in early brain development (see picture). More...

Sharma, A., Durrleman, S. , Gilmore, J.H. , Gerig, G. Longitudinal growth modeling of discrete-time functions with application to DTI tract evolution in early neurodevelopment. Proc. of 9th IEEE International Symposium on Biomedical Imaging (ISBI May'12), p.1397-1400. N. Sadeghi, M. Prastawa, J. H. Gilmore, W. Lin, and G. Gerig, "Spatio-Temporal Analysis of Early Brain Development," in Proceedings IEEE Asilomar Conference on Signals, Systems & Computers, Nov. 2010, in print

DTIFiberTractStatistics.png

Quantitative Description of White Matter Fiber Tracts

Ongoing: Updated (8/2012) As part of this project, we are processing DTI data and computing statistics along white matter fiber tracts. For this, we are building a command line tool which allows the user to study the behavior of water diffusion along the length of the tracts. More...

Intracranial evo.png

Smooth Growth Trajectories from Time Series Shape Data

Ongoing (Updated 08/16/12): Clinical research is interested in the spatiotemporal analysis of changes of anatomical shapes and structures, which potentially leads to improved understanding of the rate of change, locality and growth trajectory of structures of interest. This project develops a new methodology for the generation of a continuous growth model generated from a sparse set of shapes. More...


Fishbaugh, J., Durrleman, S., Gerig, G. Estimation of Smooth Growth Trajectories with Controlled Acceleration from Time Series Shape Data. Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI '11). September 2011.


UtahAtlasSegmentation.png

Atlas Based Brain Segmentation

Ongoing: Automatic segmentation can be performed reliably using priors from brain atlases and an image generative model. We have developed a tool that provides an automatic segmentation pipeline in a modular framework. Input are arbitrary number of image channels and a normative statistical brain atlas representing the population. More...


Cbg-dtiatlas-tracts.png

Group Analysis of DTI Fiber Tracts

Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. More...

Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.

Reg main.png

Evaluation of Registration Algorithms

We are interested in comparing existing registration packages to determine how registration in Slicer3 can be improved. This work focuses on examining various packages researchers are currently using for registration and comparing results on a set of examples representative of common registration tasks. More...


Regtestbedmain.png

A Framework for Registration Evaluation and Exploration

In response to the difficulties of image registration, we propose an environment where registration applications can be explored, tested, and compared. More...

LesionSegmentation.png

Lesion Segmentation

Quantification, analysis and display of brain pathology such as white matter lesions as observed in MRI is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes and for developing new therapies. More...

Marcel Prastawa and Guido Gerig. Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic Region Partitioning. 3D Segmentation in the Clinic: A Grand Challenge II Workshop at Medical Image Computing and Computer Assisted Intervention (MICCAI) 2008.


DTINoiseStatistics.png

Influence of Imaging Noise on DTI Statistics

Clinical acquisition of diffusion weighted images with high signal to noise ratio remains a challenge. The goal of this project is to understand the impact of MR noise on quantiative statistics of diffusion properties such as anisotropy measures, trace, etc. More...

Meningiomasim iter1.jpg

Tumor Simulation for Validating Change Tracking Applications

Determining extent of pathology as it changes over time is an important clinical task. However, there is a lack of a reliable, objective ground truth for evaluating automatic tracking methods. We have developed a simulation tool that can generate MR images with known tumor and edema. More...