An assessment of the owned canine and feline demographics in Chile: registration, sterilization, and unsupervised roaming indicators.

The global rise in companion animal populations, particularly dogs and cats, is driven by emotional and social benefits for owners, and their population management is becoming critically important to avoid a plethora of adverse effects on themselves, humans, and wildlife. We estimated the size and density of the owned canine and feline population in Chile and evaluated the status of microchipping, registration, sterilization rates, and the proportion of owned animals that roam unsupervised. A cross-sectional household survey in 36 districts was conducted and standard inferential statistics was employed to analyze differences between cats and dogs, sexes within each species, and between rural and urban areas. Additionally, two negative binomial models with mixed effects were developed to predict the number of dogs and cats per households. Two methods were used to compare population size estimates at the country level, multiplying: (1) the estimated mean number of companion animals per household by the estimated number of households at the country level, and (2) the estimated human:dog and human:cat ratios by the total human population. The study involved 6333 respondents, of which 76% (74% urban; 83% rural) owned companion animals (dogs and/or cats). Individuals in rural multi-person households increase the probability of owning dogs and/or cats. Additionally, women exhibit a greater inclination towards cat and dog ownership compared to men, while those over 30 years old demonstrate lower rates of companion animal ownership in contrast to the 18-30 age group for both species. The overall human:dog and human:cat ratios estimated were 2.7:1, and 6.2:1, respectively. The estimated total number of owned dogs and cats in Chile ranged from 9.6 to 10.7 million, depending on the methodological approach, while national median density of companion animals was 12 dogs per km2 (ranging from 0.02 to 7232) and 5 cats per km2 (ranging from 0.01 to 3242). This nationwide study showed one of the highest percentages of households with companion animals in Latin America and relatively low registration and sterilization rates, highlighting the need to strength long-term public policies to control populations of companion animals and promote responsibility in pet ownership.

Deformation field correction for spatial normalization of PET images.

Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.

Deformation field correction for spatial normalization of PET images using a population-derived partial least squares model.

Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet work on anatomically accurate PET-to-PET registration is limited. We present a method for the spatial normalization of PET images that improves their anatomical alignment based on a deformation correction model learned from structural image registration. To generate the model, we first create a population-based PET template with a corresponding structural image template. We register each PET image onto the PET template using deformable registration that consists of an affine step followed by a diffeomorphic mapping. Constraining the affine step to be the same as that obtained from the PET registration, we find the diffeomorphic mapping that will align the structural image with the structural template. We train partial least squares (PLS) regression models within small neighborhoods to relate the PET intensities and deformation fields obtained from the diffeomorphic mapping to the structural image deformation fields. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation based evaluation on 79 subjects shows that our method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.