UNLABELLED: Probabilistic atlases are more representative of the population than single brain atlases. They allow anatomic and functional labeling of the results of group studies in stereotactic space and, hence, the automated anatomic labeling of individual brain imaging data. METHODS: In the current study, probabilistic maps of the blood flow distribution of the middle cerebral artery (MCA) were developed using the basal and MCA brain SPECT images. Twenty-nine patients (mean age +/- SD, 54.6 +/- 6.1 y) who previously received placement of a stent for MCA stenosis (right MCA stenosis, 15 patients; left MCA stenosis, 14 patients) were included in the current study. Of the 29 MCA SPECT images, 18 were analyzed for the final result because 11 MCA SPECT images revealed an uneven uptake distribution of (99m)Tc-ethylcysteinate dimer in the brain. MCA brain SPECT images were coregistered to basal brain SPECT images, and spatial normalization parameters used for basal brain SPECT images were reapplied to MCA brain SPECT for anatomic standardization. Pixel counts of the MCA brain SPECT images were then normalized, and the probabilistic map of cerebral perfusion distribution (perfusion probabilistic map) for each hemisphere was determined by averaging the spatial- and count-normalized MCA brain SPECT images. Population-based probabilistic maps representing the extent of MCA territory (extent probabilistic map) were also composed by averaging the binary images obtained by thresholding the spatially normalized MCA brain SPECT images. RESULTS: The blood supply from the MCA to the basal ganglia area was largest (probability, 0.6 approximately 0.8), followed by the insular cortex (probability, 0.3 approximately 0.5), and various cerebral cortical areas (probability, 0.2 approximately 0.4). The MCA reached to deep structures of the brain, including the internal capsule, caudate nucleus, putamen, globus pallidus, insular cortex, and thalamus with a high-extent probability. CONCLUSION: A population-based probabilistic map of MCA flow distribution was generated by using MCA brain SPECT images. This map could be a potential tool for the analysis of major cerebral artery distribution, especially the MCA. Furthermore, the probabilistic MCA atlas could be used to define the object delineation of the MCA territory, to quantify ischemic disease affecting the MCA, to predict prognosis, and to risk stratification of cerebrovascular diseases, especially affecting the MCA.
UNLABELLED: Probabilistic atlases are more representative of the population than single brain atlases. They allow anatomic and functional labeling of the results of group studies in stereotactic space and, hence, the automated anatomic labeling of individual brain imaging data. METHODS: In the current study, probabilistic maps of the blood flow distribution of the middle cerebral artery (MCA) were developed using the basal and MCA brain SPECT images. Twenty-nine patients (mean age +/- SD, 54.6 +/- 6.1 y) who previously received placement of a stent for MCA stenosis (right MCA stenosis, 15 patients; left MCA stenosis, 14 patients) were included in the current study. Of the 29 MCA SPECT images, 18 were analyzed for the final result because 11 MCA SPECT images revealed an uneven uptake distribution of (99m)Tc-ethylcysteinate dimer in the brain. MCA brain SPECT images were coregistered to basal brain SPECT images, and spatial normalization parameters used for basal brain SPECT images were reapplied to MCA brain SPECT for anatomic standardization. Pixel counts of the MCA brain SPECT images were then normalized, and the probabilistic map of cerebral perfusion distribution (perfusion probabilistic map) for each hemisphere was determined by averaging the spatial- and count-normalized MCA brain SPECT images. Population-based probabilistic maps representing the extent of MCA territory (extent probabilistic map) were also composed by averaging the binary images obtained by thresholding the spatially normalized MCA brain SPECT images. RESULTS: The blood supply from the MCA to the basal ganglia area was largest (probability, 0.6 approximately 0.8), followed by the insular cortex (probability, 0.3 approximately 0.5), and various cerebral cortical areas (probability, 0.2 approximately 0.4). The MCA reached to deep structures of the brain, including the internal capsule, caudate nucleus, putamen, globus pallidus, insular cortex, and thalamus with a high-extent probability. CONCLUSION: A population-based probabilistic map of MCA flow distribution was generated by using MCA brain SPECT images. This map could be a potential tool for the analysis of major cerebral artery distribution, especially the MCA. Furthermore, the probabilistic MCA atlas could be used to define the object delineation of the MCA territory, to quantify ischemic disease affecting the MCA, to predict prognosis, and to risk stratification of cerebrovascular diseases, especially affecting the MCA.
Authors: Jinhao Lyu; Ning Ma; David S Liebeskind; Danny J J Wang; Lin Ma; Yang Xu; Ting Wang; Zhongrong Miao; Xin Lou Journal: Stroke Date: 2016-01-05 Impact factor: 7.914
Authors: Lucy J Garvey; Nicola Pavese; Anil Ramlackhansingh; Emma Thomson; Joanna M Allsop; Marios Politis; Ranjababu Kulasegaram; Janice Main; David J Brooks; Simon D Taylor-Robinson; Alan Winston Journal: PLoS One Date: 2012-07-12 Impact factor: 3.240