Respiratory gating is used for reducing the effects of breathing motion in a wide range of applications from radiotherapy treatment to diagnostical imaging. Different methods are feasible for respiratory gating. In this study seven gating methods were developed and tested on positron emission tomography (PET) listmode data. The results of seven patient studies were compared quantitatively with respect to motion and noise. (1) Equal and (2) variable time-based gating methods use only the time information of the breathing cycle to define respiratory gates. (3) Equal and (4) variable amplitude-based gating approaches utilize the amplitude of the respiratory signal. (5) Cycle-based amplitude gating is a combination of time and amplitude-based techniques. A baseline correction was applied to methods (3) and (4) resulting in two new approaches: Baseline corrected (6) equal and (7) variable amplitude-based gating. Listmode PET data from seven patients were acquired together with a respiratory signal. Images were reconstructed applying the seven gating methods. Two parameters were used to quantify the results: Motion was measured as the displacement of the heart due to respiration and noise was defined as the standard deviation of pixel intensities in a background region. The amplitude-based approaches (3) and (4) were superior to the time-based methods (1) and (2). The improvement in capturing the motion was more than 30% (up to 130%) in all subjects. The variable time (2) and amplitude (4) methods had a more uniform noise distribution among all respiratory gates compared to equal time (1) and amplitude (3) methods. Baseline correction did not improve the results. Out of seven different respiratory gating approaches, the variable amplitude method (4) captures the respiratory motion best while keeping a constant noise level among all respiratory phases.

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...

Motion is a source of degradation in positron emission tomography (PET)/computed tomography (CT) images. As the PET images represent the sum of information over the whole respiratory cycle, attenuation correction with the help of CT images may lead to false staging or quantification of the radioactive uptake especially in the case of small tumors. We present an approach avoiding these difficulties by respiratory-gating the PET data and correcting it for motion with optical flow algorithms. The resulting dataset contains all the PET information and minimal motion and, thus, allows more accurate attenuation correction and quantification.

The problem of motion is well known in positron emission tomography (PET) studies. The PET images are formed over an elongated period of time. As the patients cannot hold breath during the PET acquisition, spatial blurring and motion artifacts are the natural result. These may lead to wrong quantification of the radioactive uptake. We present a solution to this problem by respiratory-gating the PET data and correcting the PET images for motion with optical flow algorithms. The algorithm is based on the combined local and global optical flow algorithm with modifications to allow for discontinuity preservation across organ boundaries and for application to 3-D volume sets. The superiority of the algorithm over previous work is demonstrated on software phantom and real patient data.

Respiratory gating is the method of dividing the data from a tomographic scan with respect to the respiratory phase of the patient. It enables more accurate images by reducing the effects of motion blur and attenuation artifacts due to motion. However, it induces image degradation due to higher noise levels as the number of events per gate is reduced. Due to lack of systematic studies in this regard, different numbers of gates are being used in the scientific and clinical practice. The present study aims at examining the relationship between the respiratory signal, the number of gates required for accurate motion detection, and the level of noise with two different methods of gating: (1) Amplitude-based gating and (2) time-based gating. Patient data with a wide range of motion are used for the study. The results show that time-based gating underestimates the real respiratory displacement by up to 50%. The optimal number of gates is 8 for amplitude- and 6 for time-based gatings. The noise properties remain the same with either method but noise increases with increasing number of gates.