Determination of the Intracranial Volume: A Registration Approach

Hentschel, Sven; Kruggel, Frithjof

doi:10.1007/978-3-540-28626-4_31

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…An evaluation of the ICV estimation procedure yielded an error for the IC volume of less than 1.5% (Hentschel and Kruggel, 2004). Phantom-based validation studies (Alfano et al, 1997) found a variance of 2.6 -3.2% between phantom volumes and segmented compartments.…”

Section: Discussionmentioning

confidence: 98%

“…A mask of the intracranial volume was generated for each dataset by a registration approach (Hentschel and Kruggel, 2004). A non-linear registration between a T 1 -weighted dataset of a reference and a study subject yields a deformation field (Christensen, 1996;Wollny and Kruggel, 2001).…”

Section: Image Acquisitionmentioning

confidence: 99%

“…The bottommost slice was set 10 mm below the base of the cerebellum, as suggested by Lemieux et al (1999). An evaluation of this procedure in a group of 12 dual-weighted datasets using a ''leave-one-out'' strategy demonstrated an expected error for the IC volume of less than 1.5% (Hentschel and Kruggel, 2004). An example segmentation is shown in Fig.…”

Section: Image Acquisitionmentioning

confidence: 99%

See 2 more Smart Citations

MRI-based volumetry of head compartments: Normative values of healthy adults

Kruggel

2006

NeuroImage

117

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The size of head compartments (head and brain volume, intracranial volume, gray and white matter volume, cerebrospinal fluid volume) and their ratios were determined on the basis of magnetic resonance images of the head acquired in a reference population of 502 healthy subjects. Age-matched subgroups were selected to reveal genderrelated differences and changes with age. Normative data are provided in the form of simple equations that allow transforming measured compartment volumes into z scores, offering the possibility to relate individual data to a larger population. D

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Section: Discussionmentioning

confidence: 98%

Section: Image Acquisitionmentioning

confidence: 99%

Section: Image Acquisitionmentioning

confidence: 99%

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MRI-based volumetry of head compartments: Normative values of healthy adults

Kruggel

2006

NeuroImage

117

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“…A quality check was performed on the successful conversion and registration. The outer hulls of the brain were removed by a registration approach [24], yielding the “peeled” brain in the intracranial space (IC). Subsequently, all brain datasets were registered with the MNI 152 brain using a recent approach for nonlinear registration based on fluid dynamics [25].…”

Section: Methodsmentioning

confidence: 99%

Empirical derivation of the reference region for computing diagnostic sensitive 18fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample

Rasmussen

Lakatos

Erp

et al. 2012

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Diseas

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Careful selection of the reference region for non-quantitative positron emission tomography (PET) analyses is critically important for Region of Interest (ROI) data analyses. We introduce an empirical method of deriving the most suitable reference region for computing neurodegeneration sensitive 18fluorodeoxyglucose (FDG) PET ratios based on the dataset collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Candidate reference regions are selected based on a heat map of the difference in coefficients of variation (COVs) of FDG ratios over time for each of the Automatic Anatomical Labeling (AAL) atlas regions normalized by all other AAL regions. Visual inspection of the heat map suggests the portion of the cerebellum and vermis superior to the horizontal fissure is the most sensitive reference region. Analyses of FDG ratio data show increases in significance on the order of ten-fold when using the superior portion of the cerebellum as compared with the traditionally used full cerebellum. The approach to reference region selection in this paper can be generalized to other radiopharmaceuticals and radioligands as well as to other disorders where brain changes over time are hypothesized and longitudinal data is available. Based on the empirical evidence presented in this study, we demonstrate the usefulness of the COV heat map method and conclude that intensity normalization based on the superior portion of the cerebellum may be most sensitive to measuring change when performing longitudinal analyses of FDG-PET ratios as well as group comparisons in Alzheimer's disease.

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“…The co-occurrence matrices have been widely used in texture analysis for identification of tissue or to detect the abnormality within an organ tissue and in segmentation problems [8], [9], [10].…”

Section: Image Featuresmentioning

confidence: 99%

Hemorrhage detection in MRI brain images using images features

Moraru¹,

Moldovanu²,

Bibicu³

et al. 2013

AIP Conference Proceedings

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Abstract. The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid -sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

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Determination of the Intracranial Volume: A Registration Approach

Cited by 12 publications

References 21 publications

MRI-based volumetry of head compartments: Normative values of healthy adults

MRI-based volumetry of head compartments: Normative values of healthy adults

Empirical derivation of the reference region for computing diagnostic sensitive 18fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample

Hemorrhage detection in MRI brain images using images features

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