Improving Y PET Scan Image Quality Through Optimized Reconstruction Algorithms.

This study aimed to improve the quality of Y PET imaging by optimizing the reconstruction algorithm. We recruited 10 patients with neuroendocrine tumor metastatic to the liver or primary hepatocellular carcinoma who were qualified for Y-labeled selective internal radiation therapy or peptide receptor radionuclide therapy. They underwent posttherapeutic PET/CT imaging using 3 different reconstruction parameters: VUE Point HD with a 6.4-mm filter cutoff, 24 subsets, and 2 iterations (algorithm A); VUE Point FX with a 6.0-mm filter cutoff, 18 subsets, and 3 iterations using time of flight (algorithm B); and VUE Point HD (LKYG) with a 5-mm filter cutoff, 32 subsets, and 1 iteration (algorithm C). The reconstructed PET/CT images were assessed by 10 nuclear medicine physicians using 4-point semiqualitative scoring criteria. A value of less than 0.05 was considered significant. The median quality assessment scores for algorithm C were consistently scored the highest, with algorithms A, B, and C, scoring 3, 2, and 4, respectively. The Y PET scans using algorithm C were deemed diagnostic 91% of the time. There was a statistically significant difference in quality assessment scores among the algorithms by the Kruskal-Wallis rank sum test ([Formula: see text] = 86.5, < 0.001), with a mean rank quality score of 130.03 for algorithm A, 109.76 for algorithm B, and 211.71 for algorithm C. Subgroup analysis for quality assessment scoring of post-peptide receptor radionuclide therapy imaging alone showed a statistically significant difference between different scanning algorithms ([Formula: see text] = 35.35, < 0.001), with mean rank quality scores of 45.85 for algorithm A, 50.05 for algorithm B, and 85.6 for algorithm C. Similar results were observed for quality assessment scoring of imaging after selective internal radiation therapy ([Formula: see text] = 79.90, < 0.001), with mean ranks of 82.33 for algorithm A, 55.79 for algorithm B, and 133.38 for algorithm C. The new LKYG algorithm that was featured by decreasing the number of iterations, decreasing the cutoff of the filter thickness, and increasing the number of subsets successfully improved image quality.