Sanida Mustafovic | PubMed Health

PubMed2006

Correction of head movement on PET studies: comparison of methods.

UNLABELLED: Head movement presents a continuing problem in PET studies. Head restraint minimizes movement but is unreliable, resulting in the need to develop alternative strategies. These include frame-by-frame (FBF) realignment or use of motion tracking (MT) during the scan to realign PET acquisition data. Here we present a comparative analysis of these 2 methods of motion correction. METHODS: Eight volunteers were examined at rest using (11)C-raclopride PET with the radioligand administered as a bolus followed by constant infusion to achieve steady state. Binding potential (BP) was estimated using the ratio method during 2 periods of the scan at steady state. Head movement was compensated by using coregistration between frames (FBF) and 3 methods using MT measurements of head position acquired with a commercially available optical tracking system. RESULTS: All methods of realignment improved test-retest reliability and noise characteristics of the raw data, with important consequences for the power to detect small changes in radiotracer binding, and the potential to reduce false-positive and false-negative results. MT methods were superior to FBF realignment using coregistration on some indices. CONCLUSION: Such methods have considerable potential to improve the reliability of PET data with important implications for the numbers of volunteers required to test hypotheses.

Algorithms↗

PubMed2004

Object dependency of resolution in reconstruction algorithms with interiteration filtering applied to PET data.

In this paper, we study the resolution properties of those algorithms where a filtering step is applied after every iteration. As concrete examples we take filtered preconditioned gradient descent algorithms for the Poisson log likelihood for PET emission data. For nonlinear estimators, resolution can be characterized in terms of the linearized local impulse response (LLIR). We provide analytic approximations for the LLIR for the class of algorithms mentioned above. Our expressions clearly show that when interiteration filtering (with linear filters) is used, the resolution properties are, in most cases, spatially varying, object dependent and asymmetric. These nonuniformities are solely due to the interaction between the filtering step and the Poisson noise model. This situation is similar to penalized likelihood reconstructions as studied previously in the literature. In contrast, nonregularized and postfiltered maximum-likelihood expectation maximization (MLEM) produce images with nearly "perfect" uniform resolution when convergence is reached. We use the analytic expressions for the LLIR to propose three different approaches to obtain nearly object independent and uniform resolution. Two of them are based on calculating filter coefficients on a pixel basis, whereas the third one chooses an appropriate preconditioner. These three approaches are tested on simulated data for the filtered MLEM algorithm or the filtered separable paraboloidal surrogates algorithm. The evaluation confirms that images obtained using our proposed regularization methods have nearly object independent and uniform resolution.

Algorithms↗