Decomposing the Hounsfield unit
PurposeThe aim of this study was to present and evaluate a standardized technique for brain segmentation of cranial computed tomography (CT) using probabilistic partial volume tissue maps based on a database of high resolution T1 magnetic resonance images (MRI).MethodsProbabilistic tissue maps of wh...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
21 January 2012
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| In: |
Clinical neuroradiology
Year: 2012, Jahrgang: 22, Heft: 1, Pages: 79-91 |
| ISSN: | 1869-1447 |
| DOI: | 10.1007/s00062-011-0123-0 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/s00062-011-0123-0 Verlag, Volltext: https://link.springer.com/article/10.1007/s00062-011-0123-0 
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| Verfasserangaben: | A. Kemmling, H. Wersching, K. Berger, S. Knecht, C. Groden, I. Nölte |
MARC
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| 520 | |a PurposeThe aim of this study was to present and evaluate a standardized technique for brain segmentation of cranial computed tomography (CT) using probabilistic partial volume tissue maps based on a database of high resolution T1 magnetic resonance images (MRI).MethodsProbabilistic tissue maps of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) were derived from 600 normal brain MRIs (3.0 Tesla, T1-3D-turbo-field-echo) of 2 large community-based population studies (BiDirect and SEARCH Health studies). After partial tissue segmentation (FAST 4.0), MR images were linearly registered to MNI-152 standard space (FLIRT 5.5) with non-linear refinement (FNIRT 1.0) to obtain non-binary probabilistic volume images for each tissue class which were subsequently used for CT segmentation. From 150 normal cerebral CT scans a customized reference image in standard space was constructed with iterative non-linear registration to MNI-152 space. The inverse warp of tissue-specific probability maps to CT space (MNI-152 to individual CT) was used to decompose a CT image into tissue specific components (GM, WM, CSF).ResultsPotential benefits and utility of this novel approach with regard to unsupervised quantification of CT images and possible visual enhancement are addressed. Illustrative examples of tissue segmentation in different pathological cases including perfusion CT are presented.ConclusionAutomated tissue segmentation of cranial CT images using highly refined tissue probability maps derived from high resolution MR images is feasible. Potential applications include automated quantification of WM in leukoaraiosis, CSF in hydrocephalic patients, GM in neurodegeneration and ischemia and perfusion maps with separate assessment of GM and WM. | ||
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