Automatic Quantification for Phantom-based Image Quality Assessment in Bone Spect: Computerized Self-classification of Detectability Using a Novel Index

Abstract Background: We have previously developed a custom-design thoracic bone scintigraphy-specific phantom (hereafter referred to as “SIM2 bone phantom”) to assess image quality in bone single-photon emission computed tomography (SPECT). This study aimed to develop an automatically assessment system for imaging technology in bone SPECT and to demonstrate the validity of this system.Methods:We were able to generate more realistic phantom configurations of the thorax, spine, mediastinum, and lung. Four fillable spheres as bone metastases with diameters of 13, 17, 22, and 28 mm were inserted in the vertebral body. The newly developed software with statistical parametric mapping version 2 was automatically calculated for quantitative indexes (e.g., contrast-to-noise ratio, % coefficient of variance, % detectability equivalence volume, sharpness index). The repeatability and reproducibility of the contrast-to-noise ratio was assessed and compared between the automatic and manual methods. A detectability score (DS) was used to define the four observation types to score the hot spherical lesions whichwere classified with the quantitative indexes using decision tree analysis. The gold standardfor DSs was independently classified by three expert board-certified nuclear medicine technologists using a 4-point classification (1, poor; 2, average; 3, adequate; 4, excellent); thereafter, a consensus was reached. To assess the validity of the DS, the DS classified with Hone Graph was compared to the visual evaluation by 11 technologists (using the same score).Results: The repeatability and reproducibility of the software were shown to be excellent. Decision tree analysis produced seven terminal groups, and four quantitative indexes were used to classifying the DS. The automatically classified DSs exhibited an almost perfect agreement with the gold standard. The agreement between the Hone Graph and the experienced (n = 4), moderately experienced (n = 3), and inexperienced (n = 4) observerswas almost perfect, substantial, and moderate, respectively.Conclusions: The developed software could automatically classify the detectability of hot lesions in the SIM2 bone phantom using the self-calculated quantitative indexes. These findings suggest that this novel software could provide a means to automatically perform analysis after data input that is both excellent in convenience and repeatability.