View source for BRAINS Automated Pipeline

'''Objective:'''

Develop tools to eliminate the need for manual intervention for the analysis of brain morphology using MR images. We currently have a semiautomated pipeline that includes the following steps
* AC-PC Alignment of T1 Volume
* Co-registration of T2 weighted images to AC-PC Aligned T1  
* Bias Field Correction
* Tissue Classification
* Talairach Atlas Segmentation
* Neural Network Segmentation
* Surface Generation
* Measurements of Volumetric and Surface Data 

'''Proposed Pipeline:'''
*Alignment of T2 to raw T1 scan - Use BRAINSFit
*Brain Extraction - BRAINSMush
*Tissue Classification - KMeans + Bayesian
*Bias Field Correction - MRBiasCorrector
*AC-PC Detection T1 - ART
*Resample T1 Image - TBD
*AC-PC Detection T2 - ART
*Alignment of T2 to AC-PC aligned T1 - BrainsFit Initialized with ART results
*Brain Extraction - BRAINSMush + BRAINSCut
*Tissue Classification - TBD
*Talairach Brain Labeling - TBD
*Neural Network Labeling - BRAINSCut
*Measurements - TBD
*Surface Generation - BrainSurf
*Surface Labeling - TBD

'''Progress:'''
*A beta version of the automated workup exists within BRAINS2. 
*BRAINS3 has been created using a TCL command line to connect components from BRAINS2, ITK, and VTK
**Nearly all code is in ITK with a couple of exceptions: ROIs and Talairach Parameters
**Interfaces have been created to seamlessly handle BRAINS and ITK images using ITK filters
**Requires the WrapITK Interface
*Added a few new classes required for wrapping and utilization via the command line
*Added improved interpolation schemes including Sinc and B-Spline
**Will become the standard for images used for tissue classification


'''To Do:'''
*Refine data types used in WrapITK to decrease the memory footprint
*Improve TCL scripts to verify that memory is being freed appropriately
*Look into loading only certain aspects of ITK when they are needed
*Implement Tissue classification in ITK
*Handle Talairach parameters using VTK structured grids
*Use new ROI format, used by BRAINSTracer, for working with ROIs
*Integrate some features through command line with BRAINSTracer


'''Key Investigators:'''

* University of Iowa: Vincent Magnotta, Hans Johnson, Greg Harris, Wen Li, Steven Dunn, Nancy Andreasen
* The MIND Institute: Jeremy Bockholt


'''Links:'''
*[http://www.psychiatry.uiowa.edu University of Iowa Department of Psychiatry]
*[http://mri.radiology.uiowa.edu University of Iowa MRI Center]
*[http://www.themindinstitute.org The MIND Institute]

'''Papers:'''
#Magnotta VA, Harris G, Andreasen NC, O'Leary DS, Yuh WT, Heckel D. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T5K-45CDCB4-1&_user=440026&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000020939&_version=1&_urlVersion=0&_userid=440026&md5=83d35d3c9fad3a23586cbc69bb4a3c01 Structural MR image processing using the BRAINS2 toolbox]. Comput Med Imaging Graph. 26(4):251-64, 2002.
#Harris G, Andreasen NC, Cizadlo T, Bailey JM, Bockholt HJ, Magnotta VA, Arndt S. [http://ovidsp.tx.ovid.com/spb/ovidweb.cgi?QS2=434f4e1a73d37e8c8b3eab7e2fc8fd7c6cd285333102903abf6db4d0383fe185b1afeb5c8fe460849111d41b74f1bb2fe25edd5c9c1568de7fb679ca1c59ab5cb6bc187b207e5413628d683d042ba3f3e22279d57e7f3f2fc0c750eb9bd0e507e1f8467d2b0b3b3eb6afc2f173900b843530b33c23d53f0621f46d1d1ae2a170575352b64c16b1c8bcbb976bced869ada0278b7fdae2642675f6aebdd6ea57742601bbc6f5b7631238523c0c04f267b2095da4018d35e1b722318cd6ccfc41b9a7bdb84c821d1a20387fbf59dac3510c04fd22f344b6ee6f441992349d5ba18429d7e1aa8165bb3675121a2666271ab8974afed6a71cb7a3e77b4ba81387800121a46c25806d343e1c21c366dab7f2f712c48d6706467c883c9cf8b9b31625d93991dc44b6a284d02413e3eee7eee016e7bfe2dd34893ea0a81b0bd392f7520f437a08b615f794fa6f00065c78548d9e0d0d806a591f3e1b98a8bbee752e110315b8ccc58b195cb4250b80a4a4d4c1f101de8518d45043347dc04ed477052755955e0370cb1bfc0e745204900e213558fbb72d3ae39505ca Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection]. J Comput Assist Tomogr. 23(1):144-54, 1999.
#Magnotta VA, Andreasen NC, Schultz SK, Harris G, Cizadlo T, Heckel D, Nopoulos P, Flaum M. [http://cercor.oxfordjournals.org/cgi/content/full/9/2/151 Quantitative in vivo measurement of gyrification in the human brain: changes associated with aging]. Cereb Cortex. 9(2):151-60, 1999.
#Powell S, Magnotta VA, Johnson H, Jammalamadaka VK, Pierson R, Andreasen NC. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4PGGP30-3&_user=440026&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000020939&_version=1&_urlVersion=0&_userid=440026&md5=c783cac6caa49b7253ab0d08c9da068b Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures].  Neuroimage. 39(1):238-47, 2008.
#Magnotta VA, Heckel D, Andreasen NC, Cizadlo T, Corson PW, Ehrhardt JC, Yuh WT. [http://radiology.rsnajnls.org/cgi/content/full/211/3/781 Measurement of brain structures with artificial neural networks: two- and three-dimensional applications].  Radiology. 211(3):781-90, 1999.