Abstract
BACKGROUND AND OBJECTIVE: This study aimed to assess the impact of upsampling and downsampling techniques on the noise characteristics and similarity metrics of scintigraphic images in nuclear medical imaging. METHODS: A physical phantom study using dynamic imaging was used to generate reproducible static images of varying count statistics. Naïve upsampling and downsampling with linear interpolation were compared against alternative methods based on the preservation of Poisson count statistics and principles of nuclear scintigraphic imaging; namely, linear interpolation with a Poisson resampling correction (upsampling) and a sliding window summation method (downsampling). For each resizing method, we computed the similarity of resized images to count-matched images acquired at the target grid size with the structural similarity index measure and the logarithm of the mean squared error. These image quality metrics were subsequently compared to those of two independent count-matched images at the target grid size (representing variance due to natural noise permutations) as a reference to establish an optimal resizing method. RESULTS: Only upsampled images with the Poisson resampling correction after linear interpolation produced images that were similar to those acquired at the target grid size. For downsampling, both linear interpolation and sliding window summation yielded similar outcomes for a reduction factor of 2. However, for a reduction factor of 4, only sliding window summation resulted in image similarity metrics in agreement with those at the target grid size. CONCLUSIONS: The study underlines the importance of applying appropriate resizing techniques in nuclear medical imaging to produce realistic images at the target grid size.