Noise suppressed partial volume correction for cardiac SPECT/CT

Abstract

Purpose: Partial volume correction (PVC) methods typically improve quantification at the expense of increased image noise and reduced reproducibility. In this study, the authors developed a novel voxel-based PVC method that incorporates anatomical knowledge to improve quantification while suppressing noise for cardiac SPECT/CT imaging.

Methods: In the proposed method, the SPECT images were first reconstructed using anatomical-based maximum a posteriori (AMAP) with Bowsher's prior to penalize noise while preserving boundaries. A sequential voxel-by-voxel PVC approach (Yang's method) was then applied on the AMAP reconstruction using a template response. This template response was obtained by forward projecting a template derived from a contrast-enhanced CT image, and then reconstructed using AMAP to model the partial volume effects (PVEs) introduced by both the system resolution and the smoothing applied during reconstruction. To evaluate the proposed noise suppressed PVC (NS-PVC), the authors first simulated two types of cardiac SPECT studies: a (99m)Tc-tetrofosmin myocardial perfusion scan and a (99m)Tc-labeled red blood cell (RBC) scan on a dedicated cardiac multiple pinhole SPECT/CT at both high and low count levels. The authors then applied the proposed method on a canine equilibrium blood pool study following injection with (99m)Tc-RBCs at different count levels by rebinning the list-mode data into shorter acquisitions. The proposed method was compared to MLEM reconstruction without PVC, two conventional PVC methods, including Yang's method and multitarget correction (MTC) applied on the MLEM reconstruction, and AMAP reconstruction without PVC.

Results: The results showed that the Yang's method improved quantification, however, yielded increased noise and reduced reproducibility in the regions with higher activity. MTC corrected for PVE on high count data with amplified noise, although yielded the worst performance among all the methods tested on low-count data. AMAP effectively suppressed noise and reduced the spill-in effect in the low activity regions. However it was unable to reduce the spill-out effect in high activity regions. NS-PVC yielded superior performance in terms of both quantitative assessment and visual image quality while improving reproducibility.

Conclusions: The results suggest that NS-PVC may be a promising PVC algorithm for application in low-dose protocols, and in gated and dynamic cardiac studies with low counts.

FIG. 1.

(a) Illustration of the SPECT detectors and the central slice of the sample normalized attenuation weighted sensitivity map. (b) The profile plot along the arrow shown in (a).

FIG. 2.

(a) Simulated contrast CT. (b) Simulated SPECT MPI study and the corresponding template derived from (a). (c) Simulated SPECT RBC study and the corresponding template derived from (a). The ROI mean values in the templates were estimated from iMTC results. Note for the MPI study, the defect (as denoted by the arrows) only shows in the myocardium of the SPECT simulation but not in the contrast CT and its derived template.

FIG. 3.

Segmented ROIs overlaid on the contrast CT of the dog study in different views. The ROIs used to create the template are labeled on the sagittal slice.

FIG. 4.

Sample reconstructed images (first row), corresponding bias (middle row) and standard deviation images (bottom row) of the NCAT MPI simulation studies, with (a) high counts and (b) low counts. The bias images were calculated as the absolute differences between the phantom and the mean images of 30 noise realizations. The standard deviation images were computed as the ensemble standard deviations of 30 noise realizations.

FIG. 5.

Sample reconstructed images (first row), corresponding bias (middle row) and standard deviation images (bottom row) of the NCAT RBC simulation studies, with (a) high counts and (b) low counts. The bias images were calculated as the absolute differences between the phantom and the mean images of 30 noise realizations. The standard deviation images were computed as the ensemble standard deviations of 30 noise realizations.

FIG. 6.

The % bias versus % CoV_s plots for the low count MPI and RBC studies in all the regions. Note the data point of MTC is not shown in this figure due to its bias and CoV_s are much higher than the displayed range, and in the RBC blood pool figure, the symbols of Yang (iMTC) and Yang (uncorrected) are overlapped. The arrows denote the parameters used to generate the images shown in Figs. 4 and 5.

FIG. 7.

The sagittal slices of the equilibrium cardiac ^99mTc-RBC study in dog reconstructed using different acquisition durations (count levels). All the SPECT images are displayed in same color scale. The same slice of the contrast CT is shown on the last column. The enlarged views show the diameters of the carotid artery and aorta measured on this slice.