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 (DSsoft) using an automatic quantification package for bone SPECT (Hone Graph) and a detectability score classified by visual assessment (DSvisual), and considered the feasibility of applying this software to various TNR images. 99mTc solution was filled into a SIM2 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 (κw) coefficient between DSsoft and DSvisual 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 κw coefficients between DSsoft and DSvisual were 0.75, 0.97, and 0.93 for TNRs 4, 6, and 8, respectively, with each κw coefficient being significant (p