A robust computational solution for automated quantification of a specific binding ratio based on [123I]FP-CIT SPECT images

• Autor(a) principal: Francisco P. M. Oliveira
• Data de Publicação: 2014
• Outros Autores: Diogo Borges Faria, Durval Campos Costa, João Manuel R. S. Tavares
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: https://hdl.handle.net/10216/70872
• topic: Ciências Tecnológicas, Ciências médicas e da saúde; Technological sciences, Medical and Health sciences

Aim The purpose of the current paper is to present a computational solution to accurately quantify a specific to a non-specific uptake ratio in [123I] FP-CIT single photon emission computed tomography (SPECT) images and simultaneously measure the spatial dimensions of the basal ganglia, also known as basal nuclei. A statistical analysis based on a reference dataset selected by the user is also automatically performed. Methods The quantification of the specific to non-specific uptake ratio here is based on regions of interest defined after the registration of the image under study with a template image. The computational solution was tested on a dataset of 38 [123I]FP-CIT SPECT images: 28 images were from patients with Parkinson's disease and the remainder from normal patients, and the results of the automated quantification were compared to the ones obtained by three well-known semi-automated quantification methods. Results The results revealed a high correlation coefficient between the developed automated method and the three semi-automated methods used for comparison (r ≥ 0.975). The solution also showed good robustness against different positions of the patient, as an almost perfect agreement between the specific to non-specific uptake ratio was found (ICC = 1.000). The mean processing time was around 6 seconds per study using a common notebook PC. Conclusions The solution developed can be useful for clinicians to evaluate [123I]FP-CIT SPECT images due to its accuracy, robustness and speed. Also, the comparison between case studies and the follow-up of patients can be done more accurately and proficiently since the intra- and inter-observer variability of the semi-automated calculation does not exist in automated solutions. The dimensions of the basal ganglia and their automatic comparison with the values of the population selected as reference are also important for professionals in this area.