Optimal energy window selection for scintigraphy and emission computed tomography

Radioisotope imaging methods such as PET, SPECT and planar scintigraphy, currently utilize a fixed energy (pulse height) acceptance window regardless of the volume of the subject being imaged. This is despite previous work suggesting that higher window settings may yield improved image quality for imaging larger objects. However, we speculate that non-standard energy windows have not been widely adopted because there has not, until now, been a method available for determining how and when to use such an approach. In this new work we address this issue and propose a method for setting an adaptive photopeak acceptance window. This should be optimal for a wide variety of imaging situations applied across different radioisotope imaging methods. In order to develop an automatic technique in which individual patient-specific optimal thresholds can be determined, we propose a Bayes' minimum error thresholding approach, which utilizes modeling the upper part of the observed energy spectrum as a two-class Gaussian mixture model. Exemplar planar Monte Carlo results and a preliminary phantom study are presented