Acetylenic pyridines for use in PET imaging of nicotinic receptors.
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Biomedical subjects
Roger Fulton
Publications and source records attributed to Roger Fulton.
Concepts of registration and correction of head motion in positron emission tomography.
The long acquisition times (up to hours) in PET brain imaging bear a high risk of head motion, which results in artefacts like blurred images and may even lead to misinterpretation and useless data. With the increased resolution of high performance PET scanners, the influence of head movements becomes more and more relevant. Especially in the analysis of small brain structures, e.g. during ROI-analysis, head motion results in inaccuracies of quantified data. This may also influence the kinetic analysis and generate artifacts in parametric images calculated from a motion-affected image sequence. This work presents the feasibility of head motion registration using an external motion tracking system. The implementation of the multi acquisition frame method and an event-by-event method to correct PET data for motion are described. The effects of motion correction are demonstrated on the basis of phantom measurements and patient data. The influence of motion correction on parametric imaging is described in a receptor study.
Artifacts↗
Performance evaluation of functional medical imaging compression via optimal sampling schedule designs and cluster analysis.
In previous work we have described a technique for the compression of positron emission tomography (PET) image data in the spatial and temporal domains based on optimal sampling schedule designs (OSS) and cluster analysis. It can potentially achieve a high data compression ratio greater than 80:1. However, the number of distinguishable cluster groups in dynamic PET image data is a critical issue for this algorithm that has not been experimentally analyzed on clinical data. In this paper, the problem of experimentally determining the ideal cluster number for the algorithm for PET brain data is addressed.
Algorithms↗
Motion artifact reduction on parametric PET images of neuroreceptor binding.
UNLABELLED: PET studies of cerebral neuroreceptors are often recorded over periods ranging from 1 to 2 h, and head movements during the studies not only lead to blurred images but also may seriously disturb the kinetic analysis. We report the effect of motion on parametric images of the distribution volume ratio (DVR), as well as possible improvements if the dynamic PET data are corrected for head movements. METHODS: The study was performed with the 5-hydroxytryptamine 2A receptor ligand (18)F-altanserin. During PET scanning, which was performed in list mode for 1 h, the position of the head was monitored by an infrared motion-tracking system. The list mode data were sorted into time frames of between 10 s and 2 min. Motion was corrected using the multiple-acquisition-frame (MAF) approach, which calculates individual attenuation files for each emission frame and its corresponding head position to avoid misalignment of transmission and emission data. After reconstruction of attenuation-corrected emission frames, each image frame was realigned to match the head position of the first frame of the emission scan. The resulting motion-corrected dynamic images were evaluated using the noninvasive Logan plot to obtain parametric images of DVR. RESULTS: DVR images of motion-affected (18)F-altanserin scans showed artifacts whose extent depended on the amount of movement. The artifacts were mainly at the border between gray matter and white matter and at the outer border of gray matter. They were seen as discontinuities and small spots whose values exceeded the expected DVR values or were even negative and that disappeared when motion correction was applied. These effects in human data were also seen on simulated (18)F-altanserin images that contained no statistical noise. CONCLUSION: Whereas the native PET images looked just blurred if the patient moved during the PET scan, parametric images of the Logan DVR, which are calculated by pixelwise linear regression, contained severe discontinuities primarily at the cortical edge. MAF-based motion correction was able to avoid these errors.
Artifacts↗
The design and implementation of a motion correction scheme for neurological PET.
A method is described to monitor the motion of the head during neurological positron emission tomography (PET) acquisitions and to correct the data post acquisition for the recorded motion prior to image reconstruction. The technique uses an optical tracking system, Polaris, to accurately monitor the position of the head during the PET acquisition. The PET data are acquired in list mode where the events are written directly to disk during acquisition. The motion tracking information is aligned to the PET data using a sequence of pseudo-random numbers, which are inserted into the time tags in the list mode event stream through the gating input interface on the tomograph. The position of the head is monitored during the transmission acquisition, and it is assumed that there is minimal head motion during this measurement. Each event, prompt and delayed, in the list mode event stream is corrected for motion and transformed into the transmission space. For a given line of response, normalization, including corrections for detector efficiency, geometry and crystal interference and dead time are applied prior to motion correction and rebinning in the sinogram. A series of phantom experiments were performed to confirm the accuracy of the method: (a) a point source located in three discrete axial positions in the tomograph field of view, 0 mm, 10 mm and 20 mm from a reference point, (b) a multi-line source phantom rotated in both discrete and gradual rotations through +/- 5 degrees and +/- 15 degrees, including a vertical and horizontal movement in the plane. For both phantom experiments images were reconstructed for both the fixed and motion corrected data. Measurements for resolution, full width at half maximum (FWHM) and full width at tenth maximum (FWTM), were calculated from these images and a comparison made between the fixedand motion corrected datasets. From the point source measurements, the FWHM at each axial position was 7.1 mm in the horizontal direction, and increasing from 4.7 mm at the 0 mm position, to 4.8 mm, 20 mm offset, in the vertical direction. The results from the multi-line source phantom with +/- 5 degrees rotations showed a maximum degradation in FWHM, when compared with the stationary phantom, of 0.6 mm, in the horizontal direction, and 0.3 mm in the vertical direction. The corresponding values for the larger rotation, +/- 15 degrees, were 0.7 mm and 1.1 mm, respectively. The performance of the method was confirmed with a Hoffman brain phantom moved continuously, and a clinical acquisition using [11C]raclopride (normal volunteer). A visual comparison of both the motion and non-motion corrected images of the Hoffman brain phantom clearly demonstrated the efficacy of the method. A sample time-activity curve extracted from the clinical study showed irregularities prior to motion correction, which were removed after correction. A method has been developed to accurately monitor the motion of the head during a neurological PET acquisition, and correct for this motion prior to image reconstruction. The method has been demonstrated to be accurate and does not add significantly to either the acquisition or the subsequent data processing.
Algorithms↗
Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects.
The generalized linear least squares (GLLS) algorithm has been found useful in image-wide parameter estimation for the generation of parametric images with positron emission tomography (PET) as it is computationally efficient and statistically reliable. However, the original algorithm was designed for parameter estimation with non-uniformly sampled instantaneous measurements. When dynamic PET data are sampled with the optimal image sampling schedule (OISS) to reduce memory and storage space, only a few temporal image frames are recorded. As a result, the direct application of GLLS is no longer appropriate. In this paper, we extend the GLLS algorithm to a five parameter model for the study of human brain metabolism, which accounts for the effect of cerebral blood volume (CBV), using OISS sampled data, with as few as five temporal samples. The formulation for this new GLLS algorithm is developed, and its computational efficiency and statistical reliability are investigated and validated using computer simulations and clinical PET [18F]-2-fluoro-2-deoxy-D-glucose (FDG) data.
Algorithms↗
Dynamic image data compression in the spatial and temporal domains: clinical issues and assessment.
In our previous work, we developed a novel approach to dynamic image data compression, and demonstrated that very high compression ratios can be achieved while preserving relevant kinetic information. However, the technique has not yet been assessed with clinical data. Many issues need to be addressed to tailor the method for clinical use. In this paper, we apply the compression technique to dynamic [18F] 2-fluoro-deoxy-glucose (FDG) brain positron emission tomography (PET) data, using a five-parameter model to include cerebral blood volume (CBV) and partial volume (PV) effects. Functional images generated from the compressed data are compared with those from the original uncompressed data. We show that the storage requirements for a typical clinical dynamic PET image data set can be reduced by more than 95%, without degradation of image quality. Furthermore, the technique greatly reduces the computational complexity of further clinical image postprocessing such as smoothing and generation of functional images. It is expected that the compression technique will be of benefit in image data management and telemedicine.
Algorithms↗