Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion can be estimated simultaneously from respiratory gated data through a joint estimation framework. However, conventional motion estimation methods based on registration of a pair of images are sensitive to noise. The goal of this study is to develop a robust joint estimation method that incorporates a deep learning (DL)-based image registration approach for motion estimation. We propose a joint estimation framework by incorporating a learned image registration network into a regularized PET image reconstruction. The joint estimation was formulated as a constrained optimization problem with moving gated images related to a fixed image via the deep neural network. The constrained optimization problem is solved by the alternating direction method of multipliers (ADMM) algorithm. The effectiveness of the algorithm was demonstrated using simulated and real data. We compared the proposed DL-ADMM joint estimation algorithm with a monotonic iterative joint estimation. Motion compensated reconstructions using pre-calculated deformation fields by DL-based (DL-MC recon) and iterative (iterative-MC recon) image registration were also included for comparison. Our simulation study shows that the proposed DL-ADMM joint estimation method reduces bias compared to the ungated image without increasing noise and outperforms the competing methods. In the real data study, our proposed method also generated higher lesion contrast and sharper liver boundaries compared to the ungated image and had lower noise than the reference gated image.

Cardiac single photon emission computed tomography (SPECT) images are known to suffer from both cardiac and respiratory motion blur. In this paper, we investigate a 4-D reconstruction approach to suppress the effect of respiratory motion in gated cardiac SPECT imaging. In this approach, the sequence of cardiac gated images is reconstructed with respect to a reference respiratory amplitude bin in the respiratory cycle. To combat the challenge of inherent high-imaging noise, we utilize the data counts acquired during the entire respiratory cycle by making use of a motion-compensated scheme, in which both cardiac motion and respiratory motion are taken into account. In the experiments, we first use Monte Carlo simulated imaging data, wherein the ground truth is known for quantitative comparison. We then demonstrate the proposed approach on eight sets of clinical acquisitions, in which the subjects exhibit different degrees of respiratory motion blur. The quantitative evaluation results show that the 4-D reconstruction with respiratory correction could effectively reduce the effect of motion blur and lead to a more accurate reconstruction of the myocardium. The mean-squared error of the myocardium is reduced by 22%, and the left ventricle (LV) resolution is improved by 21%. Such improvement is also demonstrated with the clinical acquisitions, where the motion blur is markedly improved in the reconstructed LV wall and blood pool. The proposed approach is also noted to be effective on correcting the spill-over effect in the myocardium from nearby bowel or liver activities.

Cardiac gated images in single photon emission computed tomography (SPECT) are known to suffer from increased noise due to low data counts. In this work, we investigate a post-filtering approach for SPECT images based on nonlocal means (NLM) filtering. In order to exploit the inherent correlation of the signal components among the different cardiac gates, we employ a spatiotemporal NLM filter, in which both space and temporal neighborhoods are taken into account in the similarity processing. In the experiments, we demonstrated this approach with both simulated NCAT imaging data and a set of clinical acquisition. The results show that the use of temporal smoothing in NLM could be much more effective for improving the quality of gated images than spatial smoothing.

In respiratory-gated cardiac SPECT with amplitude binning, the acquisition time can vary greatly both among respiratory gates and among acquisition angles within each gate. If not properly accounted for, this uneven distribution in acquired data statistics will lead to limited-angle artifacts in reconstruction, which in turn can impact on the accuracy of respiratory motion correction. We investigate a compensation scheme for this uneven distribution by directly taking into account in the imaging model the actual acquisition time at different projection angles. In the experiment, we demonstrated this approach with simulated NCAT imaging data, for which quantitative results were obtained on both the reconstructed myocardium and estimated motion; we also tested the proposed approach on a set of clinical acquisition. The results show that the proposed approach could effectively suppress the limited-angle artifacts and improve the reconstruction in terms of both image accuracy and lesion detectability.

Cardiac gated SPECT is an important clinical tool for assessment of both myocardial perfusion and ventricular function. Spatiotemporal (aka 4D) reconstruction has been demonstrated to be effective for suppressing the increased noise in cardiac gated SPECT. In this work, we propose a joint 4D reconstruction approach to accommodate the different respiratory phases in a dual cardiac-respiratory gating scheme in order to combat the artifacts of respiratory motion in cardiac SPECT. The proposed approach is to exploit the correlation in the signal component among both the cardiac and respiratory phases in the acquired data. In our experiments we evaluated the approach using simulated SPECT imaging with the 4D NCAT phantom and Tc-99m labeled Sestamibi as the imaging agent. Our results demonstrate that the proposed approach could effectively suppress the artifacts caused by respiratory motion in the reconstruction.

Gated cardiac SPECT imaging suffers from several degrading factors ranging from limited data counts to motion blur. In this work we investigate the use of different optical flow estimation methods for motion-compensated temporal processing in gated SPECT, of which the goal is to improve the accuracy of image reconstruction. In particular, we examine how much room is still left in optical flow estimation toward improving the reconstructed image quality. In our experiments we conducted a quantitative evaluation using simulated imaging study with multiple noise realizations from the NCAT phantom. As an upper bound, the known motion in NCAT was used for reconstruction. The results show that 4D reconstruction with a periodic optical flow model can achieve results almost matching that from the known motion.

In this work we investigate the use of total variation (TV) regularization in the context of single photon emission computed tomography (SPECT), which is currently widely used for detection and evaluation of coronary artery diseases. Owing to its piecewise smoothing property, TV regularization is known to preserve discontinuities in the resulting images in inverse problems. Our goal is to study whether such a property will be useful for improving the reconstruction accuracy of perfusion defects in cardiac SPECT, which is an important clinical task. In our evaluation study we simulated cardiac gated SPECT imaging with Tc-99m labeled sestamibi. Our results demonstrate that, compared with a traditional quadratic regularization approach, use of TV regularization could lead to improved accuracy of perfect defect detection even when perfection defects are subtle.

Recently we investigated the use of a Fourier harmonic model for determining the optical flow in a periodic image sequence, the goal of which was to exploit the temporal continuity and periodicity in the underlying motion field. In this work, we further develop this optical flow model by incorporating a spatially piecewise smoothness constraint (in the form of total variation) in order to better accommodate the discontinuity of the motion field at an object boundary. In the experiments, we demonstrate its benefits for noise reduction in motion-compensated 4D reconstruction of cardiac gated images. Our results show that it could lead to more accurate reconstruction of the myocardium in spite of strong imaging noise.