Anatomical-Based FDG-PET Reconstruction for the Detection of Hypo-Metabolic Regions in Epilepsy

Abstract

Positron emission tomography (PET) of the cerebral glucose metabolism has shown to be useful in the presurgical evaluation of patients with epilepsy. Between seizures, PET images using fluorodeoxyglucose (FDG) show a decreased glucose metabolism in areas of the gray matter (GM) tissue that are associated with the epileptogenic region. However, detection of subtle hypo-metabolic regions is limited by noise in the projection data and the relatively small thickness of the GM tissue compared to the spatial resolution of the PET system. Therefore, we present an iterative maximum-a-posteriori based reconstruction algorithm, dedicated to the detection of hypo-metabolic regions in FDG-PET images of the brain of epilepsy patients. Anatomical information, derived from magnetic resonance imaging data, and pathophysiological knowledge was included in the reconstruction algorithm. Two Monte Carlo based brain software phantom experiments were used to examine the performance of the algorithm. In the first experiment, we used perfect, and in the second, imperfect anatomical knowledge during the reconstruction process. In both experiments, we measured signal-to-noise ratio (SNR), root mean squared (rms) bias and rms standard deviation. For both experiments, bias was reduced at matched noise levels, when compared to post-smoothed maximum-likelihood expectation-maximization (ML-EM) and maximum a posteriori reconstruction without anatomical priors. The SNR was similar to that of ML-EM with optimal post-smoothing, although the parameters of the prior distributions were not optimized. We can conclude that the use of anatomical information combined with prior information about the underlying pathology is very promising for the detection of subtle hypo-metabolic regions in the brain of patients with epilepsy.

Partial Volume Correction Strategies in PET

“…Reconstruction methods that improve the effective resolution of the scanner (at a given noise level) can be considered as PVC methods. In this sense, there are numerous approaches to reconstruction that attempt to achieve the aforementioned goals [46,47]; the use of statistical iterative reconstructions has been an important step in this direction both for voxel-based [11,48] and ROI-based [49][50][51][52] reconstructions. Within the context of these algorithms (and building on them), a number of recent methods deserve to be highlighted.…”

A hybrid PET‐MRI: An integrated molecular‐genetic imaging system with HRRT‐PET and 7.0‐T MRI

“…In other words, the segmented gray matter image from MRI is projected onto the PET image of the same geometrically assigned location and then confines the molecular image data that is blurred onto the MRI's well-defined morphological data. In an actual image processing point of view, it is equivalent to the deconvolution of the measured PET sinogram with a Gaussian PSF derived from the MRI data to enhance the resolution of the PET image (Gindi et al, 1993;Baete et al, 2004;Teo et al, 2007). It is often accomplished iteratively, as shown in Figure 11.…”

The Clinical Role of Fusion Imaging Using PET, CT, and MR Imaging

“…110 This approach has many advantages because the various components of the prior, such as the pseudoPoisson nature of statistics, non-negativity of the solution, local voxel correlations (local smoothness), or known existence of anatomic boundaries, may be added one by one into the estimation process, assessed individually, and used to guarantee a fast working implementation of preliminary versions of the algorithms. A Bayesian model also can incorporate prior anatomic information derived from a registered CT 111 or MR 112,113 image in the reconstruction of PET data with the aim of avoiding resolution loss due to the regularization, exploiting the superior resolution of the anatomic images. Tc-ECD scans performed during seizure (ictal) and when the patient was seizure free (interictal) the following day are shown.…”