Magnetic resonance spectroscopy (MRS) has been widely used for studying metabolic changes in rheumatic, neurodegenerative diseases and several other types of pathologies. Nevertheless, the accurate measurement of brain metabolite concentrations is still problematic and challenging, specially for multivoxel MR Spectroscopic Imaging (MRSI) data. There is a collection of artifacts and spectra are acquired from a region containing mixed tissues: white matter (WM), grey matter (GM) and cerebrospinal uid (CSF) composition. However, the studies are interested in analyzing metabolite changes in a particular brain tissue or structure. Therefore, our work proposes a pipeline for automatic selection of spectra of interest, a subset of spectra from MRSI acquisitions based on MRI content analysis and spectral quality metrics. The proposed pipeline helps to improve multivoxel spectroscopy analysis and estimates of metabolite concentrations, by eliminating spectra outside the tissue or structure of interest and identifying noisy spectra.
KEYWORDS: Computer aided diagnosis and therapy, Magnetic resonance imaging, Data acquisition, Scanners, Computer aided design, Data modeling, Reliability, Brain, Alzheimer's disease, Data centers
Computer-aided diagnosis (CAD) tools using MR images have been largely developed for disease burden quantification, patient diagnosis and follow-up. Newer CAD tools, based on machine learning techniques, often require large and heterogeneous data-sets to provide accurate and generalizable results. Commonly multi-center MR imaging data-sets are used. Typically, collection of these data-sets require adherence to an appropriate experimental protocol in order to assure that findings are due to a pathology and not due to variability in image quality or acquisition parameters across scanners and/or imaging centers. We compared different experimental training protocols used with a representative CAD tool (in this work, designed to identify Alzheimer’s disease (AD) patients from normal control (NC) subjects) using public multi-center data-sets. We examined: 1) subsets of the data-set that were acquired on the same scanner (simulating a single site homogeneous data-set), 2) a traditional cross validation framework (i.e., randomly splitting the data-set into training and testing sets irrespective of centre), and 3) a site-wise cross validation framework, in which training and testing data were differentiated by center using a leave one center out per iteration method. Results achieved with the homogeneous data-set, traditional cross-validation and site-wise cross validation differed (p = 0.0005): 100.0% (i.e., no misclassifications), 99.6% and 97.3% accuracy rates, respectively, even when the same image data-set, features and classifier were used. The lowest accuracy was observed with site-wise cross validation, the only protocol with no site-wise contamination between training and testing samples.
Medical imaging research depends basically on the availability of large image collections, image processing and analysis algorithms, hardware and a multidisciplinary research team. It has to be reproducible, free of errors, fast, accessible through a large variety of devices spread around research centers and conducted simultaneously by a multidisciplinary team. Therefore, we propose a collaborative research environment, named Adessowiki, where tools and datasets are integrated and readily available in the Internet through a web browser. Moreover, processing history and all intermediate results are stored and displayed in automatic generated web pages for each object in the research project or clinical study. It requires no installation or configuration from the client side and offers centralized tools and specialized hardware resources, since processing takes place in the cloud.
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