Correction of liver displacement due to irregular respiration for SPECT images obtained using a multiple short-time acquisition with breath-holding technique

Irregular Respiratory Motion Compensation for Liver Contrast-Enhanced Ultrasound via Transport-Based Motion Estimation

Contrast-enhanced ultrasound (CEUS) imaging has been widely applied for the detection and characterization of focal liver lesions (FLLs), for its ability to visualize the blood flow in real time. However, cyclic liver motion poses a great challenge to the recovery of perfusion curves as well as quantitative kinetic parameters estimation. Recently, a few gating methods have been proposed to eliminate unexpected intensity fluctuations by the breathing phase estimation, with the assumption that each breathing phase corresponds to a specific lesion position strictly. While practical liver motion tends to be irregular due to changes in the patient's underlying physiologic status, that is, the same phase might not correspond to the same position. To tackle the challenge of motion irregularity, we present a novel motion estimation-based respiratory compensation method, named RCME, which first estimates salient motion information through the framework of optimal transport (OT) by jointly modeling pixel intensity as well as their locations and then employs sparse subspace clustering (SSC) to identify the subset of frames acquired at the same position. Our proposed method is evaluated on 15 clinical CEUS sequences in both qualitative and quantitative manners. Experimental results demonstrate good performance on irregular liver motion compensation.

4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECT

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.

Limited-angle effect compensation for respiratory binned cardiac SPECT

PURPOSE

In cardiac single photon emission computed tomography (SPECT), respiratory-binned study is used to combat the motion blur associated with respiratory motion. However, owing to the variability in respiratory patterns during data acquisition, the acquired data counts can vary significantly both among respiratory bins and among projection angles within individual bins. If not properly accounted for, such variation could lead to artifacts similar to limited-angle effect in image reconstruction. In this work, the authors aim to investigate several reconstruction strategies for compensating the limited-angle effect in respiratory binned data for the purpose of reducing the image artifacts.

METHODS

The authors first consider a model based correction approach, in which the variation in acquisition time is directly incorporated into the imaging model, such that the data statistics are accurately described among both the projection angles and respiratory bins. Afterward, the authors consider an approximation approach, in which the acquired data are rescaled to accommodate the variation in acquisition time among different projection angles while the imaging model is kept unchanged. In addition, the authors also consider the use of a smoothing prior in reconstruction for suppressing the artifacts associated with limited-angle effect. In our evaluation study, the authors first used Monte Carlo simulated imaging with 4D NCAT phantom wherein the ground truth is known for quantitative comparison. The authors evaluated the accuracy of the reconstructed myocardium using a number of metrics, including regional and overall accuracy of the myocardium, uniformity and spatial resolution of the left ventricle (LV) wall, and detectability of perfusion defect using a channelized Hotelling observer. As a preliminary demonstration, the authors also tested the different approaches on five sets of clinical acquisitions.

RESULTS

The quantitative evaluation results show that the three compensation methods could all, but to different extents, reduce the reconstruction artifacts over no compensation. In particular, the model based approach reduced the mean-squared-error of the reconstructed myocardium by as much as 40%. Compared to the approach of data rescaling, the model based approach further improved both the overall and regional accuracy of the myocardium; it also further improved the lesion detectability and the uniformity of the LV wall. When ML reconstruction was used, the model based approach was notably more effective for improving the LV wall; when MAP reconstruction was used, the smoothing prior could reduce the noise level and artifacts with little or no increase in bias, but at the cost of a slight resolution loss of the LV wall. The improvements in image quality by the different compensation methods were also observed in the clinical acquisitions.

CONCLUSIONS

Compensating for the uneven distribution of acquisition time among both projection angles and respiratory bins can effectively reduce the limited-angle artifacts in respiratory-binned cardiac SPECT reconstruction. Direct incorporation of the time variation into the imaging model together with a smoothing prior in reconstruction can lead to the most improvement in the accuracy of the reconstructed myocardium.

Data-driven respiratory motion tracking and compensation in CZT cameras: a comprehensive analysis of phantom and human images

BACKGROUND

This study described a method for tracking and compensating respiratory motion in cadmium-zinc-telluride (CZT) cameras. We evaluated motion effects on myocardial perfusion imaging and assessed the usefulness of motion compensation in phantom and clinical studies.

METHODS

SPECT studies were obtained from an oscillating heart phantom and 552 patients using CZT cameras with list-mode acquisition. Images were reformatted in 500-ms frames, and the activity centroid was calculated as respiratory signal. The myocardial perfusion, left ventricular (LV) wall thickness, and LV volume were assessed before and after the motion compensation technique.

RESULTS

In phantom studies, we documented only minimal bias between simulated and measured shifts. Significantly reduced tracer activity, increased wall thickness and decreased volume in scans with 15 mm or more axial shifts were noted. In clinical studies, there was a higher prevalence of significant motion after treadmill exercise. The motion compensation technique could successfully compensate those motion artifacts.

CONCLUSION

The described method allows for tracking and compensating respiratory motion in CZT cameras. Significant respiratory motion is still not uncommon using CZT cameras, especially in patients who underwent treadmill tests. Motion blurring can be compensated using image processing techniques and image quality could be significantly improved.