Clinical comparison of the positron emission tracking (PeTrack) algorithm with the real-time position management system for respiratory gating in cardiac positron emission tomography

The prevalence of image degradation due to motion in rest-stress rubidium-82 imaging on a SiPM PET-CT system

BACKGROUND

Motion of the heart is known to affect image quality in cardiac PET. The prevalence of motion blurring in routine cardiac PET is not fully appreciated due to challenges identifying subtle motion artefacts. This study utilizes a recent prototype Data-Driven Motion Correction (DDMC) algorithm to generate corrected images that are compared with non-corrected images to identify visual differences in relative rubidium-82 perfusion images due to motion.

METHODS

300 stress and 300 rest static images were reconstructed with DDMC and without correction (NMC). The 600 DDMC/NMC image pairs were assigned Visual Difference Score (VDS). The number of non-diagnostic images were noted. A "Dwell Fraction" (DF) was derived from the data to quantify motion and predict image degradation.

RESULTS

Motion degradation (VDS = 1 or 2) was evident in 58% of stress images and 33% of rest images. Seven NMC images were non-diagnostic-these originated from six studies giving a 2% rate of non-diagnostic studies due to motion. The DF metric was able to effectively predict image degradation. The DDMC heart identification and tracking was successful in all images.

CONCLUSION

Motion degradation is present in almost half of all relative perfusion images. The DDMC algorithm is a robust tool for predicting, assessing and correcting image degradation.

A preliminary evaluation of a high temporal resolution data-driven motion correction algorithm for rubidium-82 on a SiPM PET-CT system

BACKGROUND

In myocardial perfusion PET, images are acquired during vasodilator stress, increasing the likelihood of intra-frame motion blurring of the heart in reconstructed static images to assess relative perfusion. This work evaluated a prototype data-driven motion correction (DDMC) algorithm designed specifically for cardiac PET.

METHODS

A cardiac torso phantom, with a solid defect, was scanned stationary and being manually pulled to-and-fro in the axial direction with a random motion. Non-motion-corrected (NMC) and DDMC images were reconstructed. Total perfusion deficit was measured in the defect and profiles through the cardiac insert were defined. In addition, 46 static perfusion images from 36 rubidium-82 MPI patients were selected based upon a perception of motion blurring in the images. NMC and DDMC images were reconstructed, blinded, and scored on image quality and perceived motion.

RESULTS

Phantom data demonstrated near-perfect recovery of myocardial wall visualization and defect quantification with DDMC compared with the stationary phantom. Quality of clinical images was NMC: 10 non-diagnostic, 31 adequate, and 5 good; DDMC images: 0 non-diagnostic, 6 adequate, and 40 good.

CONCLUSION

The DDMC algorithm shows great promise in rubidium MPI PET with substantial improvements in image quality and the potential to salvage images considered non-diagnostic due to significant motion blurring.

A data-driven respiratory motion estimation approach for PET based on time-of-flight weighted positron emission particle tracking

Purpose

Respiratory motion of patients during positron emission tomography (PET)/computed tomography (CT) imaging affects both image quality and quantitative accuracy. Hardware‐based motion estimation, which is the current clinical standard, requires initial setup, maintenance, and calibration of the equipment, and can be associated with patient discomfort. Data‐driven techniques are an active area of research with limited exploration into lesion‐specific motion estimation. This paper introduces a time‐of‐flight (TOF)‐weighted positron emission particle tracking (PEPT) algorithm that facilitates lesion‐specific respiratory motion estimation from raw listmode PET data.

Methods

The TOF‐PEPT algorithm was implemented and investigated under different scenarios: (a) a phantom study with a point source and an Anzai band for respiratory motion tracking; (b) a phantom study with a point source only, no Anzai band; (c) two clinical studies with point sources and the Anzai band; (d) two clinical studies with point sources only, no Anzai band; and (e) two clinical studies using lesions/internal regions instead of point sources and no Anzai band. For studies with radioactive point sources, they were placed on patients during PET/CT imaging. The motion tracking was performed using a preselected region of interest (ROI), manually drawn around point sources or lesions on reconstructed images. The extracted motion signals were compared with the Anzai band when applicable. For the purposes of additional comparison, a center‐of‐mass (COM) algorithm was implemented both with and without the use of TOF information. Using the motion estimate from each method, amplitude‐based gating was applied, and gated images were reconstructed.

Results

The TOF‐PEPT algorithm is shown to successfully determine the respiratory motion for both phantom and clinical studies. The derived motion signals correlated well with the Anzai band; correlation coefficients of 0.99 and 0.94‐0.97 were obtained for the phantom study and the clinical studies, respectively. TOF‐PEPT was found to be 13–38% better correlated with the Anzai results than the COM methods. Maximum Standardized Uptake Values (SUVs) were used to quantitatively compare the reconstructed‐gated images. In comparison with the ungated image, a 14–39% increase in the max SUV across several lesion areas and an 8.7% increase in the max SUV on the tracked lesion area were observed in the gated images based on TOF‐PEPT. The distinct presence of lesions with reduced blurring effect and generally sharper images were readily apparent in all clinical studies. In addition, max SUVs were found to be 4–10% higher in the TOF‐PEPT‐based gated images than in those based on Anzai and COM methods.

Conclusion

A PEPT‐ based algorithm has been presented for determining movement due to respiratory motion during PET/CT imaging. Gating based on the motion estimate is shown to quantifiably improve the image quality in both a controlled point source phantom study and in clinical data patient studies. The algorithm has the potential to facilitate true motion correction where the reconstruction algorithm can use all data available.

Motion tracking of low-activity fiducial markers using adaptive region of interest with list-mode positron emission tomography

PURPOSE

Motion compensated positron emission tomography (PET) imaging requires detecting and monitoring of patient body motion. We developed a semiautomatic list-mode method to track the three-dimensional (3D) motion of fiducial positron-emitting markers during PET imaging.

METHODS

A previously developed motion tracking method using positron-emitting markers (PeTrack) was enhanced to work with PET imaging. A novel combination of filtering methods was developed to reject physiological tracer background, which would drown out the events from the marker if unfiltered. The most critical filter rejects events whose line-of-response (LOR) is outside an adaptive region of interest (ADROI). The size of ROI was optimized by exploiting the distinct differences between the distributions of events from background and marker. The ADROI PeTrack method was evaluated with Monte Carlo and phantom studies. A 92.5-kBq 22 Na marker moving sinusoidally in 3D was simulated with Monte Carlo methods. The simulated events were combined with list-mode data from cardiac PET imaging patients to evaluate the performance of the tracking. In phantom studies, three 22 Na markers were placed on a dynamic torso phantom with an initial activity of 680 MBq of 82 Rb in its cardiac insert. The motion of the markers was tracked while the phantom simulated various types of patient motion. Motion correction on an event-by-event basis of the list-mode data was then applied and images were reconstructed.

RESULTS

Simulation results show that the background rejection methods can significantly suppress the tracer background and increase the fraction of marker events by a factor of up to 2500. A 92.5-kBq marker can be tracked in 3D at a frequency of 2.0 Hz with an accuracy of 0.8 mm and a precision of 0.3 mm. The phantom study experimentally confirms that the algorithm can track various types of motion. The relative accuracy of the experimental tracking is 0.26 ± 0.14 mm. Motion-corrected images from the phantom study show reduced blurring.

CONCLUSIONS

An algorithm and background rejection methods were developed that can track the 3D motion of low-activity positron-emitting markers during PET imaging. The motion information may be used for motion-compensated PET imaging.