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Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tomograph

¹ Service Hospitalier Frédéric Joliot, I²BM, DSV, CEA, Orsay, France; ² Siemens Medical Solutions SAS, Saint-Denis, France; ³ School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom; ⁴ INSERM-CEA UMR 797, Service Hospitalier Frédéric Joliot, Orsay, France; and ⁵ INSERM-UPMC UMR 678, CHU Pitié-Salpêtrière, Paris, France

Correspondence: For correspondence or reprints contact: Florent C. Sureau, Service Hospitalier Frédéric Joliot, 4 Place Général Leclerc, Orsay Cedex, France 91406. E-mail: florent.sureau{at}cea.fr

Brain PET in small structures is challenged by low resolution inducing bias in the activity measurements. Improved spatial resolution may be obtained by using dedicated tomographs and more comprehensive modeling of the acquisition system during reconstruction. In this study, we assess the impact of resolution modeling (RM) during reconstruction on image quality and on the estimates of biologic parameters in a clinical study performed on a high-resolution research tomograph. Methods: An accelerated list-mode ordinary Poisson ordered-subset expectation maximization (OP-OSEM) algorithm, including sinogram-based corrections and an experimental stationary model of resolution, has been designed. Experimental phantom studies are used to assess contrast and noise characteristics of the reconstructed images. The binding potential of a selective tracer of the dopamine transporter is also assessed in anatomic volumes of interest in a 5-patient study. Results: In the phantom experiment, a slower convergence and a higher contrast recovery are observed for RM-OP-OSEM than for OP-OSEM for the same level of statistical noise. RM-OP-OSEM yields contrast recovery levels that could not be reached without RM as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with RM than without at matched resolution. In a uniform activity region, RM induces higher positive and lower negative correlations with neighboring voxels, leading to lower spatial variance. Clinical images reconstructed with RM demonstrate better delineation of cortical and subcortical structures in both time-averaged and parametric images. The binding potential in the striatum is also increased, a result similar to the one observed in the phantom study. Conclusion: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects.

Key Words: PET imaging • partial-volume effects • resolution modeling

COPYRIGHT © 2008 by the Society of Nuclear Medicine, Inc.

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JNM 2008 49: 13A-14A. [Full Text]