GATE: a simulation toolkit for PET and SPECT.

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

Improving the measurement of the 99Tc(m)-ECD brain perfusion index by temporal analysis.

Matsuda et al. have described a non-invasive method for brain perfusion quantification by computing the ratio of the cumulated counts in the cerebral hemispheres and aortic arch. The regions of interest (ROIs) are drawn manually and are observer dependent. The aim of this study was to develop a new method designed to minimize the intra- and interobserver variability when drawing the different ROIs. A dynamic study was performed as in Matsuda's method on 30 patients using technetium-99m ethyl cysteinate dimer (99Tc(m)-ECD) (ID: 800 MBq+/-33 MBq). The manual method of drawing ROIs was then compared with the following, automated one. A temporal analysis was performed on the cardiac first-pass study to obtain parametric images of the thorax. An ROI of the aortic arch was drawn automatically by means of an isocontour algorithm on the resulting views. The whole sequence was reframed and filtered by a temporal low-pass filter. Hemispheric brain ROIs were delineated on a summed image. Matsuda's algorithm was then applied. Intraobserver variability was evaluated for the classical Matsuda method. The correlation in brain perfusion index (BPI) measurements was r = 0.8976 for naive observers and r = 0.9443 for well-trained observers. Interobserver variability was also evaluated; the correlation was r = 0.7574 for naive observers and r = 0.9190 for well-trained observers. With our proposed method, the correlation in the measurements of BPI for evaluating the intraobserver variability was r = 0.9955 for naive observers and r = 0.9989 for well-trained observers. For interobserver variability, the results were r = 0.9234 for naive observers and r = 0.9230 for well-trained observers. We conclude that temporal analysis allows brain perfusion to be measured in a semi-automatic manner, and improves the reproducibility compared with the original method of Matsuda, particularly for naive observers.

Assessment of malignancy in pulmonary lesions: FDG dual-head coincidence gamma camera imaging in association with serum tumor marker measurement.

UNLABELLED: The purpose of the study was to evaluate the performance of dual-head coincidence gamma camera imaging using FDG in association with serum marker assays in identifying lung carcinoma in patients with abnormal findings on chest radiography. METHODS: A prospective evaluation of FDG imaging with coincidence detection emission tomography (CDET) using a dual-head gamma camera combined with the assessment of 3 sensitive serum markers of lung cancer (carcinoembryonic antigen, neuron specific enolase, and CYFRA 21-1) was performed on the same day on 58 consecutive patients with known or suspected lung malignancy. RESULTS: Fifty-three patients were proven to have lung cancer, and 5 patients had benign lung disease. Coincidence imaging showed significantly increased FDG uptake in 49 of 53 patients with proven malignancy (sensitivity, 92.5%) and in 3 patients with benign disease. FDG imaging had negative findings in 4 patients with proven malignancy and 2 patients with benign disease. Serum tumor marker levels were elevated in 42 of 53 cancer patients (sensitivity, 79.2%) and normal in 11 patients with proven malignancy. Nine patients with proven malignancy had positive findings on FDG images and negative marker assays. Two patients with proven malignancy had negative findings on FDG images and positive marker assays. The positive predictive value for lung cancer was 94.2% for FDG alone and 97.6% for FDG in association with serum markers. CONCLUSION: In this study, FDG CDET imaging was a powerful tool for evaluating patients with lung lesions suggestive of malignancy. Although the determination of serum marker levels was less accurate than FDG imaging, positive FDG results found in association with positive markers significantly increased the likelihood of lung malignancy.

Quantitative analysis of cholesterol and cholesteryl esters in human atherosclerotic plaques using near-infrared Raman spectroscopy.

Raman spectroscopy is a non-destructive analytical technique and previous results have shown that qualitative analysis of the lipid component of human atheromatous arteries is feasible. In this paper, we describe a quantitative analytical method for cholesterol and cholesteryl esters in human atherosclerotic plaques, combined with Raman spectroscopic results, using partial least-squares (PLS) regression, a statistical multivariate method based on factorial analysis. Twenty-nine human atherosclerotic pooled samples were studied and the results of Raman spectroscopy coupled with the PLS method were compared to biochemical results. The standard error of prediction was 16.1, 13.6, 1.9, 3.3 and 3.4 mg/g for total cholesterol, free cholesterol, palmitate cholesteryl, oleate cholesteryl and linoleate cholesteryl, respectively. The repeatability of Raman spectroscopy was found to be excellent. Our results show that Raman spectroscopy is a promising technique to obtain a consistent and non-destructive quantitative analysis of cholesterol and cholesteryl esters in human atherosclerotic lesions. In situ and in vivo analysis is a possibility in the near future.