2 research outputs found
Grid computing application for brain magnetic resonance image processing
This work emphasizes the use of grid computing and web technology for automatic postprocessing of brain magnetic resonance images (MRI) in the context of neuropsychiatric
(Alzheimer’s disease) research. Post-acquisition image processing is achieved through the
interconnection of several individual processes into pipelines. Each process has input and output
data ports, options and execution parameters, and performs single tasks such as: a) extracting
individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image
transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear
registration), c) performing image statistical analyses, and d) producing the necessary quality
control images and/or files for user review. The pipelines are built to perform specific sequences of
tasks on the alphanumeric data and MRIs contained in our database.
The web application is coded in PHP and allows the creation of scripts to create, store and execute
pipelines and their instances either on our local cluster or on high-performance computing
platforms. To run an instance on an external cluster, the web application opens a communication
tunnel through which it copies the necessary files, submits the execution commands and collects
the results.
We present result on system tests for the processing of a set of 821 brain MRIs from the
Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed
of 10 processes. Our results show successful execution on both local and external clusters, and a 4-
fold increase in performance if using the external cluster. However, the latter’s performance does
not scale linearly as queue waiting times and execution overhead increase with the number of tasks
to be executed
Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets
Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image