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O Cuisenaire

Publications and source records attributed to O Cuisenaire.

2 recordsLinked to original sources

Comparison of admission perfusion computed tomography and qualitative diffusion- and perfusion-weighted magnetic resonance imaging in acute stroke patients.

BACKGROUND AND PURPOSE: Besides classic criteria, cerebral perfusion imaging could improve patient selection for thrombolytic therapy. The purpose of this study was to compare quantitative perfusion CT imaging and qualitative diffusion- and perfusion-weighted MRI (DWI and PWI) in acute stroke patients at the time of their emergency evaluation. METHODS: Thirteen acute stroke patients underwent perfusion CT and DWI or PWI on admission. The size of infarct and ischemic lesion (infarct plus penumbra) on the admission perfusion CT was compared with that of the MR abnormalities as shown on the DWI trace and on the relative cerebral blood volume, cerebral blood flow, time to peak, and mean transit time maps calculated from PWI studies. RESULTS: The most significant correlation was found between infarct size on the admission perfusion CT and abnormality size on the admission DWI map (r=0.968, P<0.001). A significant correlation was also observed between the size of the ischemic lesion (infarct plus penumbra) on the admission perfusion CT and the abnormality size on the mean transit time map calculated from admission PWI (r=0.946, P<0.001). Information about cerebral infarct and total ischemia (infarct plus penumbra) carried by both imaging techniques was similar, with slopes of 0.913 and 0.905, respectively. CONCLUSIONS: An imaging technique may be helpful in the identification of cerebral penumbra in acute stroke patients and thus in the selection of patients for thrombolytic therapy. Perfusion CT and DWI/PWI are equivalent in this task.

Acute Disease↗

Automatic morphometry of nerve histological sections.

A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected morphological operators. By applying semantic rules to the zonal graph axon candidates are identified. Those are either isolated or still connected. Then, separation of connected fibers is performed by evaluating myelin sheath thickness around each candidate area with an Euclidean distance transformation. Finally, properties of each detected fiber are computed and false positives are removed. The accuracy of the method is assessed by evaluating missed detection, false positive ratio and comparing the results to the manual procedure with sampling. In the evaluated nerve surface, a 0.9% of false positives was found, along with 6.36% of missed detections. The resulting histograms show strong correlation with those obtained by manual measure. The noise introduced by this method is significantly lower than the intrinsic sampling variability. This automatic method constitutes an original tool for morphometrical analysis.

Animals↗