Fully 4D motion-compensated reconstruction of cardiac SPECT images

Abstract

In this paper, we investigate the benefits of a spatiotemporal approach for reconstruction of image sequences. In the proposed approach, we introduce a temporal prior in the form of motion compensation to account for the statistical correlations among the frames in a sequence, and reconstruct all the frames collectively as a single function of space and time. The reconstruction algorithm is derived based on the maximum a posteriori estimate, for which the one-step late expectation-maximization algorithm is used. We demonstrated the method in our experiments using simulated single photon emission computed tomography (SPECT) cardiac perfusion images. The four-dimensional (4D) gated mathematical cardiac-torso phantom was used for simulation of gated SPECT perfusion imaging with Tc-99m-sestamibi. In addition to bias-variance analysis and time activity curves, we also used a channelized Hotelling observer to evaluate the detectability of perfusion defects in the reconstructed images. Our experimental results demonstrated that the incorporation of temporal regularization into image reconstruction could significantly improve the accuracy of cardiac images without causing any significant cross-frame blurring that may arise from the cardiac motion. This could lead to not only improved detection of perfusion defects, but also improved reconstruction of the heart wall which is important for functional assessment of the myocardium.

Detectability of perfusion defect in five‐dimensional gated‐dynamic cardiac SPECT images

“…As a baseline for comparison, in our experiments we also reconstructed the gated images in a conventional way, in which dynamic changes in the acquired data are integrated out over separate time periods for each gating interval; next, a 4D reconstruction algorithm 33 was applied to obtain a gated image sequence for each period. Specifically, we considered the following three time periods: ͑1͒ The washin stage, which is over the first 6 min of acquisition; ͑2͒ the washout stage, which is over the second 6 min of the acquisition; and ͑3͒ the entire period, which is over the entire 12 min.…”

4D reconstruction for low-dose cardiac gated SPECT

“…In recent years, 4D reconstruction methods that exploit the temporal correlation among the gated frames have been developed to improve image quality in GSPECT, aiming to achieve better perfusion defect detectability and functional assessment. [17][18][19][20][21][22][23] Indeed, in our recent work, 23 it was demonstrated that use of temporal processing in GSPECT reconstruction can have greater impact on the accuracy of the myocardium than correction of other degrading factors. However, all these 4D studies were conducted at the standard dose level with the focus on image quality improvement.…”

The impact of audio-visual biofeedback on 4D PET images: Results of a phantom study

“…[9][10][11][12][13][14] There are four broad classes to reconstruct PET images to reduce artifacts in the presence of motion: 15 (1) hardwarebased gating, 7,[9][10][11]13,[16][17][18][19] (2) software-based gating, [20][21][22][23][24][25][26][27] (3) incorporated-motion-model (IMM) based algorithms, [28][29][30] and (4) joint estimation. [31][32][33][34] The hardware-based gating is based on the assumption that external respiratory motion is a surrogate for motion of internal structures. Investigators have used different respiration tracking systems such as pressure sensors, 17 spirometers, 17 temperature sensors, 11 and optical tracking 7,9,10 to monitor the respiratory motion and simultaneously generate a trigger signal at a user-predefined phase 7,10,11,16 or displacement.…”

“…Cao et al Gilland et al, Gravier et al proposed a maximum-a posteriori (MAP) framework in which motion information is incorporated through a temporal prior/penalty. [31][32][33] The parameter associated with the prior/penalty term often needs to be selected with careful consideration since it plays an important role in the reconstructed images. Jacobson and Fessler incorporated parameters to describe the effects of motion into the statistical model of the projections, whereby joint maximum likelihood estimation of image and motion parameters is carried out.…”