The impact of ROI definition on estimator performance on FBP reconstructed Ga-67 SPECT images

Abstract

Quantitative accuracy of reconstructed images depends not only on the reconstruction strategy, but also on the definition of the estimator. The authors investigate estimators based on: (1) a dilation of the true ROI, (2) thresholding the reconstructed image, (3) edge detection on the reconstructed image, and (4) a quasi-Gauss-Markov (QGM) estimator which partially compensates for the image acquisition and reconstruction process. The Gauss-Markov estimator is also briefly compared to these first 4 for a simpler imaging problem. The task is to find the activity in small (1 cm diameter) lesions in simulated images of thoracic gallium SPECT scans. The authors consider 35 lesion locations. Reconstruction is done with Chang-corrected FBP. The authors consider both the best accuracy achievable with each estimator and how sensitive this accuracy is to errors in the specification of the lesion's size and position. A signal-to-noise ratio (SNR) which combines both bias and variance is used as the basis for comparison. The results show that while the estimators based on a dilation of the true ROI or the thresholded image can produce very high SNR, they are both very sensitive to errors in lesion size and position. The edge-detection estimator and the QGM estimator both have lower optimal SNR, but are less sensitive to some lesion-specification errors.