Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging.

Attempts have been made to estimate a surrogate respiratory signal from the SPECT data itself, but noise, variations in attenuation with rotation, and other issues have resulted in none of these being employed routinely in a clinical setting. PURPOSE The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100 ms) SPECT projection data derived from list-mode acquisitions. METHODS For our initial evaluation we acquired list-mode projection data from an anthropomorphic cardiac phantom (Data Spectrum, Durham, NC) mounted on a Quasar respiratory motion platform (Modus Medical Devices Inc., ON, Canada) simulating 15 mm amplitude respiratory motion. We also selected three hundred and two consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson's correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements, were calculated for individual projection data sets and for the patient group as a whole. Both the VTS and COM derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom (6-dof) rigid-body motion estimation was done two ways, 1) using 3 iterations of ordered-subsets expectation-maximization (OSEM) with 4 subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (4 projection angles per subset) and 5 iterations or MLEM and 80 iterations, using the two respiratory estimates respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff were calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values). RESULTS The r value calculated when comparing the VTS and COM respiratory signals varied widely between -0.01 and 0.96 with an average of 0.70 while the SEE varied between 0.80 mm and 6.48 mm with an average of 2.05 mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11 mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r = 0.90 (0.94), and a SEE of 1.56 mm (1.20 mm) for OSEM (MLEM) respectively. Bland-Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 mm (9.2 mm) and 9.01 mm (9.14 mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 mm (14.10 mm) and 12.55 mm (14.29 mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS derived respiratory estimates to correct for respiration compared to the COM derived estimates. CONCLUSIONS The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the draw-backs of our VTS methodology; however, not generally equivalent to the VTS method in extent of correction. This article is protected by copyright. All rights reserved.