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

Abstract

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...

Data-driven head motion correction for PET using time-of-flight and positron emission particle tracking techniques

“…TOF-PEPT [ 28 ] is a modified version of the PEPT algorithm originally proposed by Parker et al [ 31 ] The algorithm determines the location of the centroid of an LOR distribution within a region-of-interest (ROI) by iteratively estimating the point in space p m that minimizes a weighted sum of LOR related distances. Mathematically, …”

“…Lu et al [ 24 , 25 ] recently presented a multi-step COM-based approach that used frame-based reconstructed image registration to obtain a transformation matrix that was then applied to perform event-based correction. In previous work on respiratory motion correction [ 26 – 28 ], we introduced a time-of-flight (TOF) weighted positron emission particle tracking (PEPT) algorithm for motion tracking which we compared against the state-of-the-art from the literature [ 24 , 25 ]. In this paper, we present a novel, fully automated methodology based on this algorithm for head motion detection and subsequent event-based correction using external markers.…”