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1.  Development of a Research Dedicated Archival System (TARAS) in a University Hospital 
Journal of Digital Imaging  2010;24(5):864-873.
Recent healthcare policies have influenced the manner in which patient data is handled in research projects, and the regulations concerning protected health information have become significantly tighter. Thus, new procedures are needed to facilitate research while protecting the confidentiality of patient data and ensuring the integrity of clinical work in the expanding environment of electronic files and databases. We have addressed this problem in a university hospital setting by developing the Tampere Research Archival System (TARAS), an extensive data warehouse for research purposes. This dynamic system includes numerous integrated and pseudonymized imaging studies and clinical data. In a pilot study on asthma patients, we tested and improved the functionality of the data archival system. TARAS is feasible to use in retrieving, analyzing, and processing both image and non-image data. In this paper, we present a detailed workflow of the implementation process of the data warehouse, paying special attention to administrative, ethical, practical, and data security concerns. The establishment of TARAS will enhance and accelerate research practice at Tampere University Hospital, while also improving the safety of patient information as well as the prospects for national and international research collaboration. We hope that much can be learned from our experience of planning, designing, and implementing a research data warehouse combining imaging studies and medical records in a university hospital.
PMCID: PMC3180537  PMID: 21042830
PACS; Research PACS; Hospital information systems; Research Archival System; TARAS; Medical research; Large scale; Pseudonymization
2.  Effect of slice thickness on brain magnetic resonance image texture analysis 
The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients.
We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy.
Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets.
Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue.
PMCID: PMC2970603  PMID: 20955567

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