A data-driven respiratory motion correction for pediatric DMSA renal SPECT imaging: A simulation study

Technetium-99 m-dimercaptosuccinic acid (DMSA) renal cortical scintigraphy is commonly used in the evaluation of children with urinary tract infections. Pyelonephritis and post-pyelonephritic scarring manifest as renal cortical defects on DMSA renal scintigraphy, including DMSA Single Photon Emission Computed Tomography (SPECT) imaging. SPECT image quality can be degraded by blurring related to respiratory motion. Thus we hypothesize that image quality will be improved with estimation and correction of respiratory motion.

The purpose of this study is to develop and evaluate a data-driven methodology that estimates surrogate respiratory signals and then employs these surrogate signals in correction of respiratory-motion in pediatric DMSA renal SPECT imaging.

The XCAT digital anthropomorphic phantom was used with SPECT Monte Carlo simulation to form a population of 100 ms projections of DMSA renal SPECT imaging acquired with clinically relevant count-levels. These 100 ms projections emulated the framing of list-mode acquisitions at Boston Children's Hospital (BCH). The axial (superior/inferior) center-of-count-mass (aCOM) approach was utilized to estimate a surrogate respiratory signal for combining the 100 ms projections into seven respiratory-motion states with each having different extents of motion. The motion-states were then reconstructed and rigid-body respiratory-motion of the kidneys between the three motion-states on either side of the center state versus the center state was estimated by rigid-body registration. This estimated motion was then used to correct respiratory motion as part of a second pass through reconstruction of the projections of the motion states. To evaluate the surrogate signal, Pearson's correlation coefficient was calculated between the true respiratory signals used in creating the XCAT projection data and the surrogate respiratory signals. The respiratory motion corrected reconstructions and the images reconstructed without respiratory motion compensation were quantitatively compared to the ground truth images (where no respiratory motion was simulated) using the Normalized Root Mean Square Error (NRMSE) as a measure of fidelity.

The average over our entire population of XCAT phantoms of Pearson's correlation coefficient (r) between the aCOM estimated surrogate respiratory and the actual average motion simulated for each of the 100 ms time intervals was 0.76. The average standard error of the estimate (SEE) for this r-value was 3.06 mm. For the group of XCAT phantoms with a simulated average amplitude of motion between 6-10 mm, there were no significant differences in the NRMSE versus ground truth reconstructions for the reconstructions with either the estimated or true motion correction methods compared against reconstructions without motion correction. For the groups with simulated average amplitudes of motion between 10-14 mm, 14-18 mm, and >18 mm, there were significant differences in the NRMSE for the reconstructions with motion with either the estimated and true motion correction applied in comparison to reconstructions without motion correction.

Respiratory motion correction in pediatric renal SPECT imaging using a data-driven approach can improve image quality, with potential for improved diagnostic accuracy for studies with a moderate amount of motion.