The Impact of Noisy and Misaligned Attenuation Maps on Human-Observer Performance at Lesion Detection in SPECT

We have demonstrated an improvement due to attenuation correction (AC) at the task of lesion detection in thoracic SPECT images. However, increased noise in the transmission data due to aging sources or very large patients, and misregistration of the emission and transmission maps, can reduce the accuracy of the AC and may result in a loss of lesion detectability. We investigated the impact of noise in and misregistration of transmission data, on the detection of simulated Ga-67 thoracic lesions. Human-observer localization-receiver-operating-characteristic (LROC) methodology was used to assess performance. Both emission and transmission data were simulated using the MCAT computer phantom. Emission data were reconstructed using OSEM incorporating AC and detector resolution compensation. Clinical noise levels were used in the emission data. The transmission-data noise levels ranged from zero (noise-free) to 32 times the measured clinical levels. Transaxial misregistrations of 0.32, 0.63, and 1.27 cm between emission and transmission data were also examined. Three different algorithms were considered for creating the attenuation maps: filtered backprojection (FBP), unbounded maximum-likelihood (ML), and block-iterative transmission AB (BITAB). Results indicate that a 16-fold increase in the noise was required to eliminate the benefit afforded by AC, when using FBP or ML to reconstruct the attenuation maps. When using BITAB, no significant loss in performance was observed for a 32-fold increase in noise. Misregistration errors are also a concern as even small errors here reduce the performance gains of AC.