Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution

Abstract

Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-Of-Distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer 18F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in Superior-Inferior (SI) and Anterior-Posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated reconstructions, there was no significant difference between COD-based and Anzai-based methods. Finally, data-driven COD-based event-by-event respiratory motion correction yielded comparable results to that based on Anzai respiratory traces, in terms of contrast recovery and reduced motion-induced blur. Data-driven event-by-event respiratory motion correction using COD showed significant image quality improvement compared with reconstructions with no motion correction, and gave comparable results to the Anzai-based method.

Respiratory-resolved MR-based attenuation correction for motion-compensated cardiac PET-MR

“…In contrast to a respiratory belt, which measures the movement of the abdominal or chest wall, that surrogate provided a direct displacement measure of respiratory heart motion and is not impaired by signal saturation or phase shifts between anteriorposterior motion of the thorax and the dominant foot-head motion of the heart. Nevertheless, a range of different techniques have been proposed, which obtain a respiratory surrogate signal directly from the PET data and would not require any additional MR data acquisition (Cal-González et al 2017;Manber et al 2015;Ren et al 2017).…”

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

“…[10][11][12] Data-driven approaches make use of the raw emission data collected by the scanner to derive the respiratory signal. [13][14][15][16][17][18][19][20][21][22][23] Respiratory gating with hardware approaches requires that the signals of these devices must be temporally synchronized with the raw emission data; data-driven approaches obviate this requirement. Additionally, data-driven approaches have the potential to reduce the complexity of patient preparation prior to imaging and mitigate loss of respiratory signal due to technical issues with the gating equipment.…”

Patient motion correction for dynamic cardiac PET: Current status and challenges

“…Therefore, a data-driven method, which directly extracts motion signal from PET raw data, can be more clinically appealing instead. Data-driven methods have been proposed by many groups for respiratory 16 and cardiac motion tracking 17 while a reliable way of detecting body motion in high temporal resolution in order to correct intra-frame motion, especially in context of rapid tracer distribution change, is yet to be developed.…”