Automatic quantification package (Hone Graph) for phantom-based image quality assessment in bone SPECT: computerized automatic classification of detectability.

OBJECTIVE

We previously developed a custom-design thoracic bone scintigraphy-specific phantom ("SIM 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.

METHODS

Four spherical lesions of 13-, 17-, 22-, and 28-mm diameters in the vertebrae of SIM 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.

RESULTS

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.

CONCLUSIONS

The developed software automatically classified the detectability of hot lesions in the SIM 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.