[Feasibility of Adapting Various Tumor-to-normal Bone Ratio Images on an Automatic Quantification Package for Phantom-based Image Quality Assessment in Bone SPECT].

We investigated the impact of the tumor-to-normal bone ratio (TNR) on the concordance rate between a detectability score classified by software (DS) using an automatic quantification package for bone SPECT (Hone Graph) and a detectability score classified by visual assessment (DS), and considered the feasibility of applying this software to various TNR images. Tc solution was filled into a SIM bone phantom to achieve TNRs of 4, 6, and 8, performed by dynamic SPECT acquisitions performed for 12 minutes; reconstructions were performed using ordered subset expectation maximization at timepoints ranging from 4 to 12 minutes. This yielded a total of 384 lesions (96 SPECT images). We investigated the weighted kappa (κ) coefficient between DS and DS at various TNRs and evaluated the change in analysis accuracy before and after applying newly created analysis parameters. DSs were defined on a 4-point scale (4: excellent, 3: adequate, 2: average, 1: poor), and visual evaluations were conducted by three board-certified nuclear medicine technologists. The κ coefficients between DS and DS were 0.75, 0.97, and 0.93 for TNRs 4, 6, and 8, respectively, with each κ coefficient being significant (p<0.01). In the TNR 4 image group, κ coefficients significantly increased with the implementation of new parameters proposed in this study. We concluded that the software's automatic analysis would be closer to a visual assessment within the TNR range of 4-8 and that applying new parameters derived from this study to images with TNR 4 further improves the software's automatic analysis accuracy of DS. We suggest that software will be a useful tool for optimizing bone SPECT imaging techniques.