Linear CT-scan measurements of cerebral ventricles in senile Poodle dogs.
Glauce Vaz Diniz AraújoPaulo de Souza-JúniorShirley Viana PeçanhaCarlos Augusto Dos Santos-SousaMarcia Torres RamosFernanda Coelho Simas BernardesMarcelo Abidu FigueiredoPublished in: Brazilian journal of veterinary medicine (2023)
Breed traits seem to influence the dimensions of the cerebral ventricles in dogs. The ratios between the ventricles and the brain are crucial diagnostic criteria for suspected canine cognitive dysfunction (CCD). This study aimed to establish linear computed tomography (CT)-scan measurements of the cerebral ventricles in 55 Poodle dogs aged >7 years. To this end, cross-sectional CT images were evaluated. The measurements in the whole sample were: height of the right ventricle, 6.0 ± 1.6 mm; height of the left ventricle, 5.8 ± 1.6 mm; width of the right ventricle, 6.9 ± 1.4 mm; width of the left ventricle, 7.0 ± 1.3 mm; height of the third ventricle, 3.4 ± 0.8 mm; height of the right cerebral hemisphere, 39.5 ± 2.0 mm; and height of the left cerebral hemisphere, 40.2 ± 2.6 mm. The average ventricular measurements were higher in dogs older than 11 years (p < 0.05). However, the average ratio of the ventricle height to the height of the brain did not reveal differences between age groups, sex, or antimeres. In addition, none of the images showed fused lateral ventricles. Thus, these data can assist in interpreting ventricle size in senile Poodle dogs (aged >7 years).
Keyphrases
- computed tomography
- body mass index
- pulmonary artery
- pulmonary hypertension
- mitral valve
- subarachnoid hemorrhage
- dual energy
- cerebral ischemia
- image quality
- positron emission tomography
- contrast enhanced
- congenital heart disease
- coronary artery
- magnetic resonance imaging
- cross sectional
- pulmonary arterial hypertension
- heart failure
- gene expression
- white matter
- resting state
- convolutional neural network
- genome wide
- physical activity
- magnetic resonance
- big data
- cerebral blood flow
- functional connectivity
- machine learning
- artificial intelligence
- single cell
- pet ct