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A Semiautomatic Image Processing Tool to Measure Small Structures in Magnetic Resonance Images of the Brain at 7 Tesla
- Source :
- BIOIMAGING
- Publication Year :
- 2016
- Publisher :
- SCITEPRESS - Science and Technology Publications, 2016.
-
Abstract
- The current availability of Magnetic Resonance (MR) systems that operate at ultra high magnetic field (⥠7 Tesla) allows the representation of anatomical structures at sub-millimeter resolution. Interestingly, small structures of the brain, such as the subfields of the hippocampus, the inner structures of the basal ganglia and of the brainstem become visible. Suitable software packages that allow analyzing and measuring such small structures are not currently readily available. We developed a semi-automated procedure to measure the thickness of the stratum radiatum and lacunosum-moleculare (SRLM) of the hippocampus. The change in the thickness of this subfield of the hippocampal formation is supposed to have a role in the pathological cognitive decline. Once we developed and validated the semiautomatic procedure on the 7T high-resolution T2*-weighted images of a healthy volunteer, we carried out a preliminary study on a population affected by Mild Cognitive Impairment to investigate the correlations of the SRLM thickness with the clinical scores of the patients, e.g. the Mini-Mental State Examination score and the Free and Cued Selective Reminding Test.
- Subjects :
- education.field_of_study
medicine.diagnostic_test
Computer science
05 social sciences
Population
Measure (physics)
Hippocampus
Magnetic resonance imaging
Image processing
Hippocampal formation
050105 experimental psychology
03 medical and health sciences
0302 clinical medicine
Nuclear magnetic resonance
medicine
0501 psychology and cognitive sciences
Brainstem
Cognitive decline
education
030217 neurology & neurosurgery
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies
- Accession number :
- edsair.doi...........c211e5ed464bb423e6809fafaf4c6d7f
- Full Text :
- https://doi.org/10.5220/0005818001240128