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We aimed to compare the measurement and simulation data of bone scintigraphy of a chest phantom using a Monte Carlo simulation to verify the accuracy of the simulated data. The SIM2 bone phantom was enclosed using 300 kBq/mL of technetium-99 m (99mTc) to represent the bone tumor and 50 kBq/mL of 99mTc to represent normal bone. Projection data were obtained using single-photon emission computed tomography (SPECT). Simulated projection data were constructed based on CT data. The contrast ratio, recovery coefficient (RC), % coefficient variation (CV), and power spectrum density (PSD) of each part were calculated from the reconstructed data. The contrast ratio and RC were equal between the actual and simulated data. Higher % CV values were noted for soft tissue than for normal bone. The PSD was equal for all frequency band ranges. Our results prove the utility of the Monte Carlo simulation for verifying various data using phantoms.

We previously developed a custom-design thoracic bone scintigraphy-specific phantom (“SIM2 bone phantom”) to assess image quality in bone single-photon emission computed tomography (SPECT). We aimed to develop an automatic assessment system for imaging technology in bone SPECT and demonstrate the validity of this system. Four spherical lesions of 13-, 17-, 22-, and 28-mm diameters in the vertebrae of SIM2 bone phantom simulating the thorax were filled with radioactivity (target-to-background ratio: 4). Dynamic SPECT acquisitions were performed for 15 min; reconstructions were performed using ordered subset expectation maximization at 3–15-min timepoints. Consequently, 216 lesions (54 SPECT images) were obtained: 120 and 96 lesions were used for software development and validation, respectively. The developed software used statistical parametric mapping to rigidly register and automatically calculate quantitative indexes (contrast-to-noise ratio, % coefficient of variance, % detectability equivalence volume, recovery coefficient, target-to-normal bone ratio, and full width at half maximum). A detectability score (DS) was used to define the four observation types (4, excellent; 3, adequate; 2, average; 1, poor) to score hot spherical lesions. The gold standard for DSs was independently classified by three experienced board-certified nuclear medicine technologists using the four observation types; thereafter, a consensus regarding the gold standard for DSs was reached. Using 120 lesions for development, decision tree analysis was performed to determine DS based on the quantitative indexes. We verified the validation of the quantitative indexes and their threshold values for automatic classification using 96 lesions for validation. The trends in the automatically calculated quantitative indices were consistent. Decision tree analysis produced four terminal groups; two quantitative indexes (% detectability equivalence volume and contrast-to-noise ratio) were used to classify DS. The automatically classified DSs exhibited an almost perfect agreement with the gold standard. The percentage agreement and kappa coefficient were 91.7% and 0.93, respectively, in 96 lesions for validation. The developed software automatically classified the detectability of hot lesions in the SIM2 bone phantom using the automatically calculated quantitative indexes, suggesting that this software could provide a means to automatically perform detectability analysis after data input that is excellent in reproducibility and accuracy.