List Mode–Driven Cardiac and Respiratory Gating in PET

Abstract

Gating methods acquiring biosignals (such as electrocardiography [ECG] and respiration) during PET enable one to reduce motion effects that potentially lead to image blurring and artifacts. This study evaluated different cardiac and respiratory gating methods: one based on ECG signals for cardiac gating and video signals for respiratory gating; 2 others based on measured inherent list mode events. Methods: Twenty-nine patients with coronary artery disease underwent a 20-min ECG-gated single-bed list mode PET scan of the heart. Of these, 17 were monitored by a video camera registering a marker on the patient's abdomen, thus capturing the respiratory motion for PET gating (video method). Additionally, respiratory and cardiac gating information was deduced without auxiliary measurements by dividing the list mode stream in 50-ms frames and then either determining the number of coincidences (sensitivity method) or computing the axial center of mass and SD of the measured counting rates in the same frames (center-of-mass method). The gated datasets (respiratory and cardiac gating) were reconstructed without attenuation correction. Measured wall thicknesses, maximum displacement of the left ventricular wall, and ejection fraction served as measures of the exactness of gating. Results: All methods successfully captured respiratory motion and significantly decreased motion-induced blurring in the gated images. The center-of-mass method resulted in significantly larger left ventricular wall displacements than did the sensitivity method (P , 0.02); other differences were nonsignificant. List mode-based cardiac gating was found to work well for patients with high 18 F-FDG uptake when the center-of-mass method was used, leading to an ejection fraction correlation coefficient of r 5 0.95 as compared with ECG-based gating. However, the sensitivity method did not always result in valid cardiac gating information, even in patients with high 18 F-FDG uptake. Conclusion: Our study demonstrated that valid gating signals during PET scans cannot be obtained only by tracking the external motion or applying an ECG but also by simply analyzing the PET list mode stream on a frame-by-frame basis. PETi s an established diagnostic tool widely appreciated in the clinical fields of oncology, neurology, cardiology, and several others. PET can show functional, metabolic, and molecular processes in vivo with a high sensitivity and offers the unique feature of absolute quantification of radiotracer distribution. However, several mathematic corrections have to be applied to the measured PET raw data before or during image reconstruction to obtain absolute quantitative data. The most important of these is attenuation correction, that is, correcting for the loss of coincidence photons due to absorption while they are traversing the human body. Accurate attenuation correction requires knowledge of attenuation values in the field of view of the scanner. In stand-alone PET scanners, this information is acquired during an additional transmission scan u...

The Use of Anatomical Information for Molecular Image Reconstruction Algorithms: Attenuation/Scatter Correction, Motion Compensation, and Noise Reduction

“…Examples of these signals are infrared sensors [98,99], respiratory bellows [100], breathing temperature [101], list-mode data itself [102] for respiratory motion, and electrocardiogram (ECG) [100] for cardiac motion. For more detailed information on gating, see [103].…”

Imaging moving heart structures with PET

“…This method has shown to be comparable to techniques based on external monitoring and has been applied to cardiac imaging with some success. 16 However, such software techniques depend on the tracer uptake in the organs of interest, and thus may not be readily applicable to coronary PET imaging, where very little activity is detected in the myocardium.…”

Automatic Cardiac Self-Gating of Small-Animal PET Data

“…Indeed, estimation of the respiratory and cardiac motions have been obtained from either the variation of the number of counts with time in the whole acquisition [9], in a few axial slices [10], in some voxels of the reconstructed images [11], or as a function of counts in large sinogram bins [12] have been obtained.…”