A method for synchronizing an external respiratory signal with a list-mode PET acquisition.

A method is proposed to synchronize positron emission tomography (PET) list-mode data with an externally recorded respiratory signal in the absence of a master clock. When the respiratory signal reaches a user-defined threshold, a trigger mark is stored in the list-mode file. After the acquisition, synchronization is achieved when the stored trigger marks are superimposed on the respiratory curve to form a horizontal line over time at the user-defined threshold. Synchronization was possible and unequivocal for ten out of ten clinical studies. The list-mode acquisition actually started approximately 40 and 4 s after acquisition initiation at the user interface of the Philips Gemini and the GE DLS PET-CT systems, respectively.

Estimation of the Rigid-Body Motion from Three-Dimensional Images Using a Generalized Center-of-Mass Points Approach.

We present an analytical method for the estimation of rigid-body motion in sets of three-dimensional SPECT and PET slices. This method utilizes mathematically defined generalized center-of-mass points in images, requiring no segmentation. It can be applied to compensation of the rigid-body motion in both SPECT and PET, once a series of 3D tomographic images are available. We generalized the formula for the center-of-mass to obtain a family of points co-moving with the object's rigid-body motion. From the family of possible points we chose the best three points which resulted in the minimum root-mean-square difference between images as the generalized center-of-mass points for use in estimating motion. The estimated motion was used to sum the sets of tomographic images, or incorporated in the iterative reconstruction to correct for motion during reconstruction of the combined projection data. For comparison, the principle-axes method was also applied to estimate the rigid-body motion from the same tomographic images. To evaluate our method for different noise levels, we performed simulations with the MCAT phantom. We observed that though noise degraded the motion-detection accuracy, our method helped in reducing the motion artifact both visually and quantitatively. We also acquired four sets of the emission and transmission data of the Data Spectrum Anthropomorphic Phantom positioned at four different locations and/or orientations. From these we generated a composite acquisition simulating periodic phantom movements during acquisition. The simulated motion was calculated from the generalized center-of-mass points calculated from the tomographic images reconstructed from individual acquisitions. We determined that motion-compensation greatly reduced the motion artifact. Finally, in a simulation with the gated MCAT phantom, an exaggerated rigid-body motion was applied to the end-systolic frame. The motion was estimated from the end-diastolic and end-systolic images, and used to sum them into a summed image without obvious artifact. Compared to the principle-axes method, in two of the three comparisons with anthropomorphic phantom data our method estimated the motion in closer agreement to than of the Polaris system than the principal-axes method, while the principle-axes method gave a more accurate estimation of motion in most cases for the MCAT simulations. As an image-driven approach, our method assumes angularly complete data sets for each state of motion. We expect this method to be applied in correction of respiratory motion in respiratory gated SPECT, and respiratory or other rigid-body motion in PET.

Streak artifact reduction in filtered backprojection using a level line-based interpolation method.

UNLABELLED: Because of the limited number of projections, the mathematic reconstruction formula of the filtered backprojection (FBP) algorithm may create an artifact that streaks reconstructed images. This artifact can be imperfectly removed by replacing the ramp filter of the FBP with an ad hoc low-pass filter, the cost being the loss of contrast and definition. In this study, a solution was proposed to increase, by computational means, the number of projections to reduce the artifact at a lower cost. The cost was a postacquisition process, which was reasonably time consuming. METHODS: The process was called interpolation of projections by contouring (IPC). First, level lines were plotted on the sinogram to delimit isocount regions; then, the regions containing the interpolated points were found, and to each point was assigned the intensity of its isocount region. Using this process, the data could be resampled, allowing an increase in the number of projections or the number of pixels by projections. A phantom study of bone scintigraphy was performed to compare the slices obtained with and without the IPC process with the true image. A clinical case was also presented. RESULTS: The phantom study showed that with the IPC process, the reconstructed slice was closer to the model, inside and outside the body, when the sinogram was resampled to multiply by 2 or 3 the number of projections, with the same number of pixels per projection. In the clinical study, the streak artifact was reduced, especially outside the body, although only a ramp filter was used. CONCLUSION: The IPC process succeeded in reducing the streak artifact. This process did not require any modification in acquisition and was not operator dependent. The increase in the number of projections is likely a necessary but not a sufficient condition to reduce the streak artifact: if not corrected, the attenuation could be a limiting factor in the removal of this artifact when the number of projections increases.

Noise removal using factor analysis of dynamic structures: application to cardiac gated studies.

UNLABELLED: Factor analysis of dynamic structures (FADS) facilitates the extraction of relevant data, usually with physiologic meaning, from a dynamic set of images. The result of this process is a set of factor images and curves plus some residual activity. The set of factor images and curves can be used to retrieve the original data with reduced noise using an inverse factor analysis process (iFADS). This improvement in image quality is expected because the inverse process does not use the residual activity, assumed to be made of noise. The goal of this work is to quantitate and assess the efficiency of this method on gated cardiac images. METHODS: A computer simulation of a planar cardiac gated study was performed. The simulated images were added with noise and processed by the FADS-iFADS program. The signal-to-noise ratios (SNRs) were compared between original and processed data. Planar gated cardiac studies from 10 patients were tested. The data processed by FADS-iFADS were subtracted to the original data. The result of the substraction was studied to evaluate its noisy nature. RESULTS: The SNR is about five times greater after the FADS-iFADS process. The difference between original and processed data is noise only, i.e., processed data equals original data minus some white noise. CONCLUSION: The FADS-iFADS process is successful in the removal of an important part of the noise and therefore is a tool to improve the image quality of cardiac images. This tool does not decrease the spatial resolution (compared with smoothing filters) and does not lose details (compared with frequential filters). Once the number of factors is chosen, this method is not operator dependent.